Gastric Cancer: Clinical Features, Screening, Diagnosis, Treatment, and Prevention.

The objective of this article is to highlight the clinical features, screening, diagnosis, treatment, and prevention of gastric cancer (GC). Early GC is often asymptomatic leading to frequent delays in diagnosis. Weight loss and persistent abdominal pain are the most common symptoms at initial diagnosis. The diagnosis of GC typically involves a combination of endoscopy, biopsy, and imaging studies. Endoscopic resection techniques are emerging as successful treatment options for early GC. Treatment options for advanced GC include surgery and chemotherapy. The first line chemotherapy for advanced GC consists of doublet therapy with a combination of platinum and fluoropyrimidines. Trastuzumab, a monoclonal antibody, is used in the treatment of human epidermal growth factor 2 positive GCs. Antiangiogenic agents and immunotherapy are also useful in the treatment of GC. Currently there are no GC screening guidelines in the United States, but they exist in other regions where there is increased prevalence of GC. Prevention strategies for GC include Helicobacter pylori eradication and adoption of a healthy diet consisting of fruits and vegetables.

A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging.

COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1 s t and 2 n d order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.

Advanced machine learning approaches for predicting permeability in reservoir pay zones based on core analyses.

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models-Decision Tree, Bagging Tree, and Extra Trees-while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d'Arc Basin, including laboratory-derived permeability (K), oil saturation ( S O ), water saturation ( S W ), grain density ( ρ g r ), porosity (φ), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R2), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra Trees emerges as the most accurate model, with an R2 of 0.976, while random forest and bagging tree exhibit slightly lower R2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity (φ) and water saturation ( S W ) as the most influential, while grain density ( ρ g r ), oil saturation ( S O ), and depth are considered less important. This study contributes to the petroleum industry's knowledge by showcasing the inadequacy of MVR and highlighting the superior performance of machine learning models, particularly Extra Trees. The proposed models employed in this study can help engineers and researchers determine reservoir permeability quickly and accurately by using a few core attributes, reducing the dependency on resource-intensive and time-consuming laboratory work.

A Diagnostic Dilemma: A Case of Complicated Pneumonia With Pyelonephritis and Subclinical Myocarditis.

A 41-year-old woman presented with a 3.5-month history of fever, weakness, productive cough, and burning micturition along with generalized weakness and significant weight loss. Chest X-ray revealed bilateral infiltrates and bilateral pleural effusion, and the workup suggested community-acquired pneumonia (CAP). However, the course was complicated by persistent fevers, elevated inflammatory markers, elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), and pelvic fluid collection. Extensive investigations, including bronchoscopy and lung biopsy, failed to identify a specific pathogen. Pulmonary vasculitis and lymphoma were ruled out. Antibiotic and corticosteroid therapy resulted in clinical improvement. While the cause remains unknown, brucellosis and aspergillosis were considered but ruled out with advanced testing. The underlying etiology remains elusive, highlighting the diagnostic challenges in CAP with atypical presentations.

Prenatal diagnosis of SLC25A24 Fontaine progeroid syndrome: description of the fetal phenotype, genotype and detection of parental mosaicism.

BACKGROUND: Fontaine progeroid syndrome (FPS, OMIM 612289) is a recently identified genetic disorder stemming from pathogenic variants in the SLC25A24 gene, encoding a mitochondrial carrier protein. It encompasses Gorlin-Chaudry-Moss syndrome and Fontaine-Farriaux syndrome, primarily manifesting as craniosynostosis with brachycephaly, distinctive dysmorphic facial features, hypertrichosis, severe prenatal and postnatal growth restriction, limb shortening, and early aging with characteristic skin changes, phalangeal anomalies, and genital malformations. CASES: All known occurrences of FPS have been postnatally observed until now. Here, we present the first two prenatal cases identified during the second trimester of pregnancy. While affirming the presence of most postnatal abnormalities in prenatal cases, we note the absence of a progeroid appearance in young fetuses. Notably, our reports introduce new phenotypic features like encephalocele and nephromegaly, which were previously unseen postnatally. Moreover, paternal SLC25A24 mosaicism was detected in one case. CONCLUSIONS: We present the initial two fetal instances of FPS, complemented by thorough phenotypic and genetic assessments. Our findings expand the phenotypical spectrum of FPS, unveiling new fetal phenotypic characteristics. Furthermore, one case underscores a potential novel inheritance pattern in this disorder. Lastly, our observations emphasize the efficacy of exome/genome sequencing in both prenatal and postmortem diagnosis of rare polymalformative syndromes with a normal karyotype and array-based comparative genomic hybridization (CGH).

The Incidence and Presentation Features of Glaucoma in Vogt-Koyanagi-Harada Syndrome: A Systematic Review and Meta-Analysis.

TOPIC: To determine the cumulative incidence and features of glaucoma in Vogt-Koyanagi-Harada (VKH) syndrome patients compared to non-glaucoma patients. CLINICAL RELEVANCE: Knowing the exact burden of secondary glaucoma in VKH could guide its screening and management in clinical practice as a part of the regular follow-up for VKH patients. METHODS: The review protocol was pre-registered on PROSPERO [CRD42023462794]. PubMed, Scopus, Web of Science, EBSCOhost, and Google Scholar were searched for studies reporting the cumulative incidence and features of glaucoma presentation in VKH. A manual search was also conducted to supplement the primary search. Subgroup analyses based on glaucoma type, VKH stage, and patients' age were conducted. All analyses were conducted using STATA. Fixed- and random-effects models were selected according to the observed heterogeneity. Studies' methodological quality was determined using the NIH tool. RESULTS: The analysis of 7084 eyes revealed a progressive increase in the cumulative incidence of secondary glaucoma over time. The cumulative incidence was lowest at VKH onset (7%) and highest at 15 years (26%). Open-angle (12%; 95%CI: 9-14%) is more common than angle-closure glaucoma (7%; 95%CI: 3-13%). Glaucoma cumulative incidence is highest in the chronic recurrent stage of VKH (33%; 95%CI: 12-59%) and among children <18 years of age (26%; 95%CI: 16-37%). Features associated with glaucoma occurrence in VKH showed comparable rates to non-glaucoma cases. However, a meta-analysis to determine risk factors of glaucoma development in VKH was not feasible secondary to the lack of adjusted risk measures in included studies. Studies' quality was questionable in 5 studies. The certainty of evidence was moderate-to-high. CONCLUSION: The cumulative incidence of glaucoma increases throughout VKH's course, with a higher tendency in children, chronic recurrent stages, and long-term follow-up. Future research should focus on examining risk factors of glaucoma development in VKH through adjusted multivariable regression models.

Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer.

BACKGROUND: Various prognostic factors are expected to refine the American Thyroid Association (ATA) recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making. METHODS: We developed two predictive models for structural incomplete response (SIR) at the one-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (i.e., number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis. RESULTS: Both Model 1 and Model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (Model 1: IDI=0.02, event NRI=42.31%; Model 2: IDI=0.07, event NRI=53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, Model 2 maintained similar performance while Model 1 did not significantly improve correct reclassification. CONCLUSION: The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.

A Toluene-induced Infundibulo-neurohypophysitis: A New Cause of Hypophysitis Secondary to Toxic Exposure.

BACKGROUND: Hypophysitis is a rare inflammatory disorder of the pituitary gland. Symptoms and signs of hypophysitis can be various, and its recognition may be challenging. Arginine vasopressin deficiency (AVP-D) due to exposure to a variety of drugs and toxic substances is rare, but some cases have been reported. Only 2 cases of AVP-D following toxic exposure to toluene, an aromatic hydrocarbon, have been reported in the literature. To our knowledge, our case represents the first description of an infundibulo neurohypophysitis (INH), manifested with AVP-D, secondary to inhalation of toluene. CASE REPORT: A 59-year-old man with an unremarkable medical history was referred to our department for headache, polyuria, and polydipsia after the inhalation of spray film containing toluene. The blood tests revealed a hyperosmolar plasma hypernatremia with normal kidney function. A desmopressin test was performed, with an improvement in water balances, blood electrolytes, and diuresis contraction. A pituitary MRI detected the absence of a normal hyperintense signal of the neuro-pituitary in the T1-weighted images. In consideration of the clinical signs and radiological imaging suggestive of INH, a therapy with desmopressin and corticosteroids was instituted, with gradual improvement of polyuria and resolution of the radiological features of INH. CONCLUSION: The exceptional finding of INH, manifested with AVP-D, following toluene inhalation could represent a new secondary cause of hypophysitis. The possibility that drugs or toxic substances never reported before could induce INH should not be excluded since the study on hypophysitis is relatively recent but emerging, predictably destined to increase exponentially in the coming years.

Interobserver variability in the histopathological evaluation of melanoma: analysis of 60 case reports.

INTRODUCTION: Since the field of dermatopathology is not an exact science, it is prone to personal subjectivity, which sometimes causes disagreements on the diagnosis and assessment of some histological features. In the case of melanoma, some variables such as regression are associated with low interobserver agreement. On the contrary, other variables such as the measurement of Breslow thickness show high reproducibility. OBJECTIVE: The main objective of our study was to investigate multiple features of 60 consecutive cases of melanoma to establish interobserver reproducibility. METHODS AND MAIN RESULTS: We conducted an observational and descriptive study at Hospital de Manises, Valencia, Spain, IVO Foundation, Valencia, Spain, and Hospital 12 de Octubre, Madrid, Spain. The mean level of agreement of all study variables was moderate (Cohen's kappa coefficient statistic = 0.5). The highest agreement corresponded to polypoid morphology, pigmentation, ulceration, and solar elastosis. On the other hand, the lowest level agreement was reached for the presence of cellular pleomorphism and tumor necrosis. CONCLUSIONS: Our mean level of agreement was moderate, which reflects that some of the measured characteristics such as cellular pleomorphism or the presence of necrosis cannot be used for future studies or must be redefined and their reproducibility, reestablished. When conducting a research study, it is necessary to analyze the study variables to demonstrate their validity to measure or classify a certain feature. It is also advisable to warrant that that the variables are reproducible to be able to use them for other studies or in the routine clinical practice.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features.

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.

Global incidence and characteristics of spinal cord injury since 2000-2021: a systematic review and meta-analysis.

BACKGROUND: This study employs systematic review and meta-analysis to explore the incidence and characteristics of spinal cord injury (SCI) between 2000 and 2021, aiming to provide the most recent and comprehensive data support for the prevention, diagnosis, treatment, and care of SCI. METHODS: Systematic searches were conducted on epidemiological studies of SCI published between January 1, 2000, and March 29, 2024. Meta-analysis, subgroup analysis, meta-regression, publication bias detection, and literature quality assessment were extensively utilized. RESULTS: The pooled results from 229 studies indicated that the overall incidence rate of SCI was 23.77 (95% CI, 21.50-26.15) per million people, with traumatic spinal cord injuries (TSCI) at a rate of 26.48 (95% CI, 24.15-28.93) per million people, and non-traumatic spinal cord injuries (NTSCI) at a rate of 17.93 (95% CI, 13.30-23.26) per million people. The incidence of TSCI exhibited a marked age-related increase and was significantly higher in community settings compared to hospital and database sources. Males experienced TSCI at a rate 3.2 times higher than females. Between 2000 and 2021, the incidence of TSCI remained consistently high, between 20 and 45 per million people, whereas NTSCI incidence has seen a steady rise since 2007, stabilizing at a high rate of 25-35 per million people. Additionally, the incidence of TSCI in developing countries was notably higher than that in developed countries. There were significant differences in the causes of injury, severity, injury segments, gender, and age distribution among the TSCI and NTSCI populations, but the proportion of male patients was much higher than that of female patients. Moreover, study quality, country type, and SCI type contributed to the heterogeneity in the meta-analysis. CONCLUSIONS: The incidence rates of different types of SCI remain high, and the demographic distribution of SCI patients is changing, indicating a serious disease burden on healthcare systems and affected populations. These findings underscore the necessity of adopting targeted preventive, therapeutic, and rehabilitative measures based on the incidence and characteristics of SCI.

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. METHODS: Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. RESULTS: The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. CONCLUSION: Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.

Group-informed attentive framework for enhanced diabetes mellitus progression prediction.

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning's advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

Objective: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. Methods: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. Results: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. Conclusion: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Morphological and Chemical Diversity within Japanese Laurencia Complex (Rhodomelaceae, Ceramiales, Rhodophyta).

Seaweeds of the red algal genus Laurencia are widely distributed worldwide in tropical, subtropical to temperate zones, and grow in Japan from Hokkaido to Okinawa. Laurencia is one of the most studied seaweeds by organic chemists because it produces a variety of compounds with unique structures. In Japan, various halogenated compounds have been found in Laurencia, while some species do not produce any halogenated compounds. Laurencia is one of the most difficult seaweeds to classify morphologically; however, the major halogenated secondary metabolites produced tend to be species-specific, and these compounds can be used as chemical markers for chemical systematics (chemotaxonomy). Similarly, it has been confirmed that domestic Laurencia species produce species-specific halogenated compounds of certain types. Laurencia is one of the "weedy seaweeds" that have not been effectively utilized at present, but it produces a wide variety of metabolites, so there is a good possibility that compounds with specific activity may be found. Thus, it can be seen that the secondary metabolites in Laurencia have many interesting aspects. In this review, we reported significant morphological features to distinguish species in this genus, and the morphological features, habitat, distribution, and chemical composition that help discriminate Japanese Laurencia species.

Comparison of clinicopathological features between cerebral cystic and alveolar echinococcosis: analysis of 27 cerebral echinococcosis cases in Xinjiang, China.

BACKGROUND: Cerebral echinococcosis is relatively rare, and it is important to distinguish cerebral cystic echinococcosis (CCE) from cerebral alveolar echinococcosis (CAE) in terms of pathological diagnosis. We aim to describe the different clinicopathological features among patients with CCE and CAE. METHODS: We collected 27 cases of cerebral echinococcosis which were diagnosed in the Department of Pathology of the First Affiliated Hospital of Xinjiang Medical University from January 1, 2012, to June 30, 2023. We compared the patients' clinical characteristics, MRI features, and pathologic manifestations of CCE and CAE. RESULTS: Among 27 cases of cerebral echinococcosis, 23 cases were CAE and 4 cases were CCE. The clinical manifestations of both CCE and CAE patients mainly included headache (21 patients, 77.78%), limb movement disorders (6 patients, 22.22%), epileptic seizures (4 patients, 14.81%) and visual disturbances (2 patients, 7.41%). The average onset age of CAE cases was 34.96 ± 11.11 years, which was 9.00 ± 7.26 years in CCE cases. All CAE patients presented with multiple involvements in the brain and extracranial organs while all CCE patients observed a solitary lesion in the brain and 3 CCE cases had no extracranial involvement. Lesions of CCE in MRI showed a single isolated circular, which was well demarcated from the surrounding tissues and with no obvious edema around the lesions, whereas CAE lesions presented as multiple intracranial lesions, with blurred edges and edema around the lesions, and multiple small vesicles could be observed in the lesions. The edge of CAE lesions could be enhanced, while CCE lesions have no obvious enhancement. CCE foci were clear cysts with a wall of about 0.1 cm. Microscopically, the walls of the cysts were characterized by an eosinophilic keratin layer, which was flanked on one side by basophilic germinal lamina cells, which were sometimes visible as protocephalic nodes. While the CAE lesion was a nodular structure with a rough and uneven nodule surface, and the cut section was cystic and solid; microscopically, the CAE lesion had areas of coagulative necrosis, and the proto-cephalic nodes were barely visible. Inflammatory cell areas consisting of macrophages, lymphocytes, epithelioid cells, plasma cells, eosinophils, and fibroblasts can be seen around the lesion. Brain tissues in the vicinity of the inflammatory cell areas may show apoptosis, degeneration, necrosis, and cellular edema, while brain tissues a little farther away from the lesion show a normal morphology. CONCLUSIONS: With the low incidence of brain echinococcosis, the diagnosis of echinococcosis and the differential diagnosis of CAE and CCE are challenging for pathologists. Grasping the different clinical pathology characteristics of CAE and CCE is helpful for pathologists to make accurate diagnoses.

Genomic characteristics and evolution of Multicentric Esophageal and gastric Cardiac Cancer.

BACKGROUND: Esophageal carcinoma (EC) and gastric cardiac adenocarcinoma (GCA) have high incidence rates in the Chaoshan region of South China. Multifocal esophageal and cardiac cancer (MECC) is commonly observed in this region in clinical practice. However, the genomic characteristics of MECC remains unclear. MATERIALS AND METHODS: In this study, a total of 2123 clinical samples of EC and GCA were analyzed to determine the frequency of multifocal tumors, as well as their occurrence sites and pathological types. Cox proportional hazards regression was used to model the relationship between age, sex, and tumor state concerning survival in our analysis of the cohort of 541 patients with available follow-up data. We performed whole-genome sequencing on 20 tumor foci and 10 normal samples from 10 MECC patients to infer clonal structure on 6 MECC patients to explore genome characteristics. RESULT: The MECC rate of EC and GCA was 5.65% (121 of 2123). Age and sex were potential factors that may influence the risk of MECC (p < 0.001). Furthermore, MECC patients showed worse survival compared with single tumor patients. We found that 12 foci from 6 patients were multicentric origin model (MC), which exhibited significant heterogeneity of variations in paired foci and had an increased number of germline mutations in immune genes compared to metastatic model. In MC cases, different lesions in the same patient were driven by distinct mutation and copy number variation (CNV) events. Although TP53 and other driver mutation genes have a high frequency in the samples, their mutation sites show significant heterogeneity in paired tumor specimens. On the other hand, CNV genes exhibited higher concordance in paired samples, especially in the amplification of oncogenes and the deletion of tumor suppressor genes. CONCLUSIONS: The extent of inter-tumor heterogeneity suggests both monoclonal and polyclonal origins of MECC, which could provide insight into the genome diversity of MECC and guide clinical implementation.

Clinical features and risk factors for mortality in patients with Klebsiella pneumoniae bloodstream infections.

INTRODUCTION: Concern about Klebsiella pneumoniae (K. pneumoniae) bloodstream infections (KP-BSIs) is widespread because of their high incidence and lethality. The aim of this study was to investigate the clinical features of, and risk factors for mortality caused by KP-BSIs. METHODOLOGY: This was a single-center retrospective observational study performed between 1 January 2019 and 31 December 2021, at a tertiary hospital. All patients with KP-BSIs were enrolled and their clinical data were retrieved from electronic medical records. RESULTS: A total of 145 patients were included (121 in the survival group and 24 in the non-survival group). There was a higher proportion of lower respiratory tract infections in the non-survival group than in the survival group (33.3% vs. 12.4%) (p < 0.05). There was a higher proportion of multi drug resistant (MDR) strains of K. pneumoniae in the non-survival group than in the survival group (41.7% vs. 16.5%) (p < 0.05). Multivariate analysis revealed that sequential organ failure assessment (SOFA) score > 6.5 (OR, 13.71; 95% CI, 1.05-179.84), admission to the intensive care unit (ICU) (OR, 2.27; 95% CI, 0.26-19.61) and gastrointestinal bleeding (OR, 19.97; 95% CI, 1.11-361.02) were independent risk factors for death in patients with KP-BSIs. CONCLUSIONS: Among all KP-BSIs, a high proportion of K. pneumoniae originated from lower respiratory tract infections, and a high proportion of K. pneumoniae were MDR; however, mortality was not influenced. SOFA score > 6.5, admission to the ICU, and gastrointestinal bleeding were independent risk factors for death in patients with KP-BSI.

A comparison of clinical characteristics and course predictors in early- and childhood-onset schizophrenia.

AIM: The aim of this study was to compare the clinical characteristics of childhood-onset schizophrenia (COS) and early-onset schizophrenia (EOS) during the first- episode psychosis and the stable period, to examine psychopharmacological treatment approaches, and to investigate potential predictive factors for prognosis. METHODS: Demographic, clinical, and psychopharmacological therapy data for 31 patients diagnosed with COS and 66 with EOS were retrieved from the file records in this multicenter study. Symptom distribution and disease severity and course were evaluated twice, in the acute psychotic stage and in the latest stable phase, during follow-up using the positive and negative syndrome scale (PANSS) and clinical global impression (CGI) scales. RESULTS: A statistically significant difference was observed between the groups' CGI improvement rates and median last stable stage PANSS positive, negative, and general psychopathology symptom scores (p = .005, p = .031, p = .005, and p = .012, respectively). Premorbid neurodevelopmental disorder and obsessive-compulsive disorder and comorbidities were more common in the COS group (p = .025 and p = .030, respectively), and treatment required greater multiple antipsychotic use in that group (p = .013). When the independent variables affecting the difference between pre- and post-treatment PANSS scores were examined using linear regression analysis, the model established was found to be statistically significant (F = 5.393; p = .001), and the group variable (p = .024), initial disease severity (p = .001), and socioeconomic level (p = .022; p = .007) emerged as predictive factors for the disease course. CONCLUSION: Although early diagnosis and treatment is an important factor in improving prognosis in schizophrenia, more specific predictors for schizophrenia need to be identified. Additionally, preventive programs and pharmacological methods need to be developed in children with neurodevelopmental problems, particularly those from low socioeconomic status families.

Clinical Features and Histopathological Analysis of Oral Lichen Planus: An Analysis of 105 Chinese Patients.

PURPOSE: To study the clinical and pathological characteristics of oral lichen planus (OLP) in a large sample. MATERIALS AND METHODS: A comprehensive analysis was conducted on 105 patients with oral lichen planus (OLP), considering various factors including sex, age, disease site, lesion type, lesion area, morphological characteristics, self-reported symptoms, and history of systemic diseases. Histopathological examination was performed for each patient, and the pathology results were analysed according to sex and age group. RESULTS: 70.5% of the OLP patients were female, and OLP was most likely to occur in the cheek, followed by the tongue, lips, gums and palate. The patients with moderate pain according to the VAS score accounted for 60%. Thirty-nine percent of the OLP patients had a systemic disease, and the most common clinical type of OLP was nonerosive. Most of the pathological results showed liquefaction degeneration of basal cells and infiltration of lamina propria lymphocytes. There was no statistically significant difference in pathological manifestations between male and female patients, and there were statistically significant differences in pathological manifestations among different ages patients. CONCLUSION: This study analysed the sociodemographic data and clinical manifestations of 105 OLP patients to guide follow-up treatment planning and disease monitoring. Moreover, pathological manifestations should be analysed to avoid delayed treatment and to monitor for carcinogenesis. Furthermore, the correlation of pathological manifestations among OLP patients with different sexes and ages is conducive to further research on the specific differential manifestations and possible underlying mechanisms involved.