A feasibility study on utilizing machine learning technology to reduce the costs of gastric cancer screening in Taizhou, China.

Aim: To optimize gastric cancer screening score and reduce screening costs using machine learning models. Methods: This study included 228,634 patients from the Taizhou Gastric Cancer Screening Program. We used three machine learning models to optimize Li's gastric cancer screening score: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL). The performance of the binary classification models was evaluated using the area under the curve (AUC) and area under the precision-recall curve (AUCPR). Results: In the binary classification model used to distinguish low-risk and moderate- to high-risk patients, the AUC in the GBM, DRF, and DL full models were 0.9994, 0.9982, and 0.9974, respectively, and the AUCPR was 0.9982, 0.9949, and 0.9918, respectively. Excluding Helicobacter pylori IgG antibody, pepsinogen I, and pepsinogen II, the AUC in the GBM, DRF, and DL models were 0.9932, 0.9879, and 0.9900, respectively, and the AUCPR was 0.9835, 0.9716, and 0.9752, respectively. Remodel after removing variables IgG, PGI, PGII, and G-17, the AUC in GBM, DRF, and DL was 0.8524, 0.8482, 0.8477, and AUCPR was 0.6068, 0.6008, and 0.5890, respectively. When constructing a tri-classification model, we discovered that none of the three machine learning models could effectively distinguish between patients at intermediate and high risk for gastric cancer (F1 scores in the GBM model for the low, medium and high risk: 0.9750, 0.9193, 0.5334, respectively; F1 scores in the DRF model for low, medium, and high risks: 0.9888, 0.9479, 0.6694, respectively; F1 scores in the DL model for low, medium, and high risks: 0.9812, 0.9216, 0.6394, respectively). Conclusion: We concluded that gastric cancer screening indicators could be optimized when distinguishing low-risk and moderate to high-risk populations, and detecting gastrin-17 alone can achieve a good discriminative effect, thus saving huge expenditures.

RNA polymerase stalling-derived genome instability underlies ribosomal antibiotic efficacy and resistance evolution.

Bacteria often evolve antibiotic resistance through mutagenesis. However, the processes causing the mutagenesis have not been fully resolved. Here, we find that a broad range of ribosome-targeting antibiotics cause mutations through an underexplored pathway. Focusing on the clinically important aminoglycoside gentamicin, we find that the translation inhibitor causes genome-wide premature stalling of RNA polymerase (RNAP) in a loci-dependent manner. Further analysis shows that the stalling is caused by the disruption of transcription-translation coupling. Anti-intuitively, the stalled RNAPs subsequently induce lesions to the DNA via transcription-coupled repair. While most of the bacteria are killed by genotoxicity, a small subpopulation acquires mutations via SOS-induced mutagenesis. Given that these processes are triggered shortly after antibiotic addition, resistance rapidly emerges in the population. Our work reveals a mechanism of action of ribosomal antibiotics, illustrates the importance of dissecting the complex interplay between multiple molecular processes in understanding antibiotic efficacy, and suggests new strategies for countering the development of resistance.

[Exploring the Causal Relationship Between Coagulation Function and Gestational Diabetes Mellitus Through Mendelian Randomization]. / å­Ÿå¾·å°”éšæœºåŒ–æŽ¢ç´¢å‡è¡€åŠŸèƒ½ä¸Žå¦Šå¨ æœŸç³–å°¿ç— çš„å› æžœå ³ç³».

Objective: To explore the causal association between coagulation function, including von Willebrand factor (vWF), a disintegrin and metalloproteinase with thrombospondin type 1 motif, member 13 (ADAMTS13), activated partial thromboplastin time (aPTT), coagulation factor Ⅷ (FⅧ), coagulation factor Ⅺ (FⅪ), coagulation factor Ⅶ (FⅦ), coagulation factor Ⅹ (FⅩ), endogenous thrombin potential (ETP), plasminogen activator inhibitor-1 (PAI-1), protein C, and plasmin, and gestational diabetes mellitus (GDM) using two-sample two-way Mendelian randomization (MR), and to provide genetic evidence for the association between coagulation function and the pathogenesis of GDM. Methods: The IEU OpenGWAS database was accessed using the R package TwoSampleMR (v 0.5.6) to obtain the statistical data of the genome-wide association study (GWAS) summary of GDM. MR analysis of the causal association between 11 coagulation function and GDM was performed by the inverse-variance weighted method (IVW), the MR-Egger method, and the weighted median method (WM). Results: In this study, the GWAS summary statistics of GDM (covering 5 687 cases and 117 892 controls) were used for MR analysis. It was found that there was a causal relationship between the predicted plasma FⅧ level and the risk for GDM (IVW: [odds ratio, OR]=0.28, 95% confidence interval [CI]: 0.10-0.75, P<0.001; WM: OR=0.30, 95% CI: 0.09-0.98, P<0.001). There was no causal relationship between other coagulation function and the risk for GDM (P>0.05). Conclusion: There is a significant causal relationship between the plasma FⅧ level and the risk for GDM. This finding highlights the complex interaction between coagulation function and glucose metabolism during pregnancy, but further research on this finding is warranted.

[Determination of five veterinary drug residues in milk by ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry based on modified chitosan membrane purification].

Milk is an important consumer product with high nutritional value. The presence of veterinary drug residues in milk owing to the indiscriminate use of veterinary drugs may affect consumer health. In the mass spectrometric analysis of trace compounds, chromatographic co-eluting components easily interfere with the mass spectral signals obtained, affecting the accuracy of qualitative and quantitative analyses. Matrix purification is a promising method to reduce the matrix effect. Chitosan is a natural biopolymer with numerous active functional groups such as amino, acetyl, and hydroxyl groups; these groups can adsorb lipids through hydrophobic and electrostatic interactions. Chitosan also has the advantages of low production cost, stable chemical properties, and convenient modification. Novel chitosan-based materials are promising candidates for lipid purification. In this study, a chitosan membrane was modified with trimethoxyoctadecylsilane (C18-CSM). C18-CSM was prepared through one-step hydrolysis and used as a dispersive solid phase extraction (DSPE) adsorbent to purify the matrix during milk pretreatment. We combined C18-CSM with ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry (UHPLC-Q/Exactive Orbitrap MS) to develop an effective method for the extraction and determination of ofloxacin, enrofloxacin, ciprofloxacin, diazepam, and metronidazole in milk. C18-CSM was characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, and water contact angle testing. The results indicated that the material has a rough surface and uniformly dense cross-section. The water contact angle of C18-CSM was 104°, indicating its good hydrophobicity. The pretreatment conditions (extraction solvent, dosage of NaCl, extraction frequency, and dosage of C18-CSM) that influenced the recoveries of the five veterinary drugs were investigated in detail. The optimal conditions were established as follows: 5% formic acid in acetonitrile, 1 g NaCl, extraction 1 time, 20 mg C18-CSM. Separation was performed on a Hypersil GOLD VANQUISH column (100 mm×2.1 mm, 1.9 µm). The mobile phase consisted of 0.1% formic acid aqueous solution and 0.1% formic acid in acetonitrile, and was flowed at a rate of 0.3 mL/min. The sample injection volume was 1 µL, and the column temperature was maintained at 25 ℃. Mass spectrometric analysis was performed in positive electrospray ionization mode. To verify the necessity of the purification material, the matrix effect was investigated using the matrix-matched standard curve method. The use of C18-CSM reduced the matrix effects of the five necessity drugs from the range of -22%-8.8% to the range of -13%-3.6%, indicating that C18-CSM is a highly efficient DSPE material. Under optimal conditions, the developed method showed good linearities within the range of 0.5-100 µg/L, with correlation coefficients (r2)≥0.9970. The limits of detection(LODs) and quantification (LOQs) were 0.2 µg/L and 0.5 µg/L, respectively. To assess the accuracy and precision of the method, we prepared milk samples with three spiked levels (low, medium, and high). The recoveries of the five veterinary drugs were ranged from 79.5% to 115%, and the intra-day and inter-day relative standard deviations were 7.0%-13% (n=6) and 1.3%-11% (n=3), respectively. This study provides a simple, accurate, and reliable method for the rapid and simultaneous determination of the five veterinary drug residues in milk.

Determination of Cannabinoids in Meat Products and Animal Feeds in Singapore Using Liquid Chromatography-Tandem Mass Spectrometry.

There has been a growing interest in the use of hemp as an animal feed ingredient considering its economic value and nutritional properties. However, there is a paucity of research regarding the safety of hemp-based animal feed currently. Thus, this raises safety concerns on the potential transfer of cannabinoids from hemp-based animal feed to animal products intended for human consumption and its health effects. As such, the detection and quantification of cannabinoids in meat and animal feeds would be desirable for monitoring purposes. In this study, a simple, rapid and sensitive method for the simultaneous quantification of four major cannabinoids (delta-9-tetrahydrocannabinol, cannabidiol, cannabinol and tetrahydrocannabinolic acid) in meat and animal feeds by liquid chromatography-tandem mass spectrometry (LC-MS/MS) was successfully developed and validated. The method was selective and sensitive, achieving limits of detection and quantification for the four cannabinoids from 5 to 7 µg/kg and 15 to 21 µg/kg, respectively. The overall recovery with matrix-matched calibration curves for the cannabinoids ranged from 87-115%. The coefficients of variation were between 2.17-13.38% for intraday precision and 3.67-12.14% for inter-day precision. The method was subsequently applied to monitor cannabinoids in 120 meat and 24 animal feed samples. No cannabinoid was detected, suggesting no imminent food safety concerns arising from the potential incorporation of hemp and by-products in animal feed and nutrition under the promotion of sustainable agricultural practices.

Risk evaluation for diabetic retinopathy in Chinese renal-biopsied type 2 diabetes mellitus patients.

AIM: To investigate diabetic retinopathy (DR) prevalence in Chinese renal-biopsied type 2 diabetes mellitus (T2DM) patients with kidney dysfunction, and to further evaluate its relationship with diabetic nephropathy (DN) incidence and the risk factors for DR development in this population. METHODS: A total of 84 renal-biopsied T2DM patients were included. Fundus and imaging examinations were employed for DR diagnosis. Demographic information and clinical measures along with renal histopathology were analyzed for comparisons between the DR and non-DR groups. Risk factors on DR development were analyzed with multiple logistic regression. RESULTS: DR prevalence was 50% in total. The incidences of DN, non-diabetic renal disease (NDRD) and mixed-type pathology were 47.6%, 19.0% and 33.3% in the DR group respectively, while 11.9%, 83.3% and 4.8% in the non-DR group. Systolic blood pressure, ratio of urinary albumin to creatine ratio, urinary albumin, 24-hours urinary protein, the incidence and severity of DN histopathology were found statistically increased in the DR group. Multiple logistic regression analysis showed histopathological DN incidence significantly increased the risk of DR development [odds ratio (OR)=21.664, 95% confidential interval (CI) 5.588 to 83.991, P<0.001 for DN, and OR=45.475, 95%CI 6.949 to 297.611, P<0.001 for mixed-type, respectively, in reference to NDRD)], wherein DN severity positively correlated. CONCLUSION: Renal histopathological evidence indicates DN incidence and severity increases the risk of DR development in Chinese T2DM patients inexperienced of regular fundus examinations.

Prolonged Disease Course Leads to Impaired Brain Function in Anxiety Disorder: A Resting State EEG Study.

Objective: Anxiety disorder (AD) is a common disabling disease. The prolonged disease course may lead to impaired cognitive performance, brain function, and a bad prognosis. Few studies have examined the effect of disease course on brain function by electroencephalogram (EEG). Methods: Resting-state EEG analysis was performed in 34 AD patients. The 34 patients with AD were divided into two groups according to the duration of their illness: anxious state (AS) and generalized anxiety disorder (GAD). Then, EEG features, including univariate power spectral density (PSD), fuzzy entropy (FE), and multivariable functional connectivity (FC), were extracted and compared between AS and GAD. These features were evaluated by three previously validated machine learning methods to test the accuracy of classification in AS and GAD. Results: Significant decreased PSD and FE in GAD were detected compared with AS, especially in the Alpha 2 band. In addition, FC analysis indicated that GAD patients' connection between the left and right hemispheres decreased. Based on machine learning, AS and GAD are classified on a six-month criterion with the highest classification accuracy of up to 0.99 ± 0.0015. Conclusion: The brain function of patients is more severely impaired in AD patients with longer illness duration. Resting-state EEG demonstrated to be a promising examination in the classification in GAD and AS using machine learning methods with better classification accuracy.

Decadal Trends in the Prevalence of Metabolic Syndrome in Economically Developed Regions in China.

Objective: To estimate decadal trends in the prevalence of metabolic syndrome (MetS) in economically developed regions in China and its association with city economic levels. Methods: Using a comprehensive Chinese healthcare database, repeated cross-sectional studies were conducted on adults who had annual health check-ups from 2012 to 2021 in 4 economically developed cities. MetS was defined by the criteria of the Chinese Diabetes Society in 2013. The crude prevalence of MetS adjusted for sex and age was reported. The association between prevalence, calendar year, and city gross domestic product (GDP) per capita was analyzed by regression model. Results: 158 274 participants aged 18 years and older were included. The unadjusted prevalence of MetS increased from 15.5% (95% CI: 14.2%-16.8%) to 20.0% (95% CI: 19.5%-20.5%) from 2012 to 2021. The adjusted overall prevalence has increased steadily from 12.8% to 20.8% after controlling age and sex (P < .001). Male and older age groups had a higher MetS prevalence. In the regression model of the association between the MetS prevalence, calendar year, and city GDP per capita, calendar year had a positive association with the prevalence (P < .001, 95% CI: 0.648-1.954) and city GDP per capita had a negative association (P = .030, 95% CI: -0.136 to -0.007). Conclusion: The MetS prevalence increased steadily in the economically developed regions in China among the health check-up population during 2012-2021. The MetS prevalence is shown to be negatively associated with GDP per capita in the study population.

Meta Learning with Attention Based FP-GNNs for Few-Shot Molecular Property Prediction.

Molecular property prediction holds significant importance in drug discovery, enabling the identification of biologically active compounds with favorable drug-like properties. However, the low data problem, arising from the scarcity of labeled data in drug discovery, poses a substantial obstacle for accurate predictions. To address this challenge, we introduce a novel architecture, AttFPGNN-MAML, for few-shot molecular property prediction. The proposed approach incorporates a hybrid feature representation to enrich molecular representations and model intermolecular relationships specific to the task. By leveraging ProtoMAML, a meta-learning strategy, our model is trained and adapted to new tasks. Evaluation on two few-shot data sets, MoleculeNet and FS-Mol, demonstrates our method's superior performance in three out of four tasks and across various support set sizes. These results convincingly validate the effectiveness of our method in the realm of few-shot molecular property prediction. The source code is publicly available at https://github.com/sanomics-lab/AttFPGNN-MAML.

Genomic and immunocyte characterisation of bloodstream infection caused by Klebsiella pneumoniae.

OBJECTIVES: The aim of this study was to evaluate the characteristics of immunocyte associated with bloodstream infection (BSI) caused by Klebsiella pneumoniae (Kpn). METHODS: Patients with BSI-Kpn were included from 2015 to 2022 in our hospital. Immunocyte subpopulations of enrolled BSI-Kpn patients were tested on the same day of blood culture using multicolor flow cytometry analysis. Antibiotic susceptibility test was determined by agar dilution or broth dilution method. All included isolates were subjected to whole genome sequencing and comparative genomics analysis. Clinical and genetic data were integrated to investigate the risk factors associated with clinical outcome. RESULTS: There were 173 patients with non-duplicate BSI-Kpn, including 81 carbapenem-resistant Kpn (CRKP), 30 extended-spectrum ß-lactamases producing Kpn (ESBL-Kpn), 62 none CRKP or ESBL-Kpn (S-Kpn). Among 68 ST11-CRKP isolates, ST11-O2v1:KL64 was the most common serotypes cluster (77.9%, 53/68), followed by ST11-OL101: KL47 (13.2%, 9/68). Compared with CSKP group, subpopulations of immunocyte in patients with CRKP were significantly lower (P < 0.01). In patients with ST11-O2v1:KL64 BSI-Kpn, the level of cytotoxic T lymphocytes (CD3 + CD8 +) is the highest, while the B lymphocytes (CD3-CD19 +) was the least. In addition, the level of immunocyte in patients with Kpn co-harbored clpV-ybtQ-qacE were lower than that in patients with Kpn harbored one of clpV, ybtQ or qacE and without these three genes. Furthermore, co-existence of clpV-ybtQ-qacE was independently associated with a higher risk for 30-day mortality. CONCLUSIONS: The results demonstrate that patients with BSI-CRKP, especially for ST11-O2v1:KL64, exhibit lower leukomonocyte counts. In addition, BSI-Kpn co-harbored clpV-ybtQ-qacE is correlated to higher 30-day mortality.

Characterization and dynamics of intestinal microbiota in patients with Clostridioides difficile colonization and infection.

Gut microbiota dysbiosis increases the susceptibility to Clostridioides difficile infection (CDI). In this study, we monitored C. difficile colonization (CDC) patients from no CDC status (CDN) to CDC status (CDCp) and CDI patients from asymptomatic status before CDI (PRECDI), CDI status (ONCDI), to asymptomatic status after CDI (POSTCDI). Based on metagenomic sequencing, we aimed to investigate the interaction pattern between gut microbiota and C. difficile. There was no significant difference of microbiota diversity between CDN and CDCp. In CDCp, Bacteroidetes and short-chain fatty acid (SCFA)-producing bacteria increased, with a positive correlation between SCFA-producing bacteria and C. difficile colonization. Compared with PRECDI, ONCDI and POSTCDI showed a significant decrease in microbiota diversity, particularly in Bacteroidetes and SCFA-producing bacteria, with a positive correlation between opportunistic pathogen and C. difficile. Fatty acid metabolism, and amino acid biosynthesis were enriched in CDN, CDCp, and PRECDI, while bile secretion was enriched in ONCDI and POSTCDI. Microbiota and metabolic pathways interaction networks in CDN and CDCp were more complex, particularly pathways in fatty acid and bile acid metabolism. Increasing of Bacteroidetes and SCFA-producing bacteria, affecting amino acid and fatty acid metabolism, is associated with colonization resistance to C. difficile and inhibiting the development of CDI.

From apparent pseudoprogression to durable complete remission of expansile destructive sinonasal mucosal melanoma under pembrolizumab after primary endoscopic resection: A case report.

Head and neck mucosal melanoma is a rare but highly aggressive malignant tumor that usually has a poor prognosis. We describe a 53-year-old male patient, having no any medical history, with left maxillary sinus mucosal melanoma causing bilateral lung metastasis. Rapid tumor regrowth was observed on the 49th day after radical tumor resection. Subsequent pembrolizumab immunotherapy initially elicited pseudoprogression, for which add-on radiation therapy was carried out during maintenance pembrolizumab. A gradual decrease in tumor volume and complete remission were observed by a series of magnetic resonance imaging scans and lung windows of a computer tomography scan of chest. At the 29-month follow-up, the patient was rendered disease-free. In conclusion, head and neck mucosal melanoma may regrow rapidly after surgical resection and pseudoprogression could be frightening during immunotherapy. Subsequent single-agent pembrolizumab plus localized radiation therapy aiming to release more tumor antigens may offer the possibility of long-term remission.

Genetic, virulence, and antimicrobial resistance characteristics associated with distinct morphotypes in ST11 carbapenem-resistant Klebsiella pneumoniae.

ST11 is the most common lineage among carbapenem-resistant Klebsiella pneumoniae (CRKP) infections in Asia. Diverse morphotypes resulting from genetic mutations are associated with significant differences in microbial characteristics among K. pneumoniae isolates. Here, we investigated the genetic determinants and critical characteristics associated with distinct morphotypes of ST11 CRKP. An ST11-KL47 CRKP isolate carrying a pLVPK-like virulence plasmid was isolated from a patient with a bloodstream infection; the isolate had the "mcsw" morphotype. Two distinct morphotypes ("ntrd" and "msdw") were derived from this strain during in vitro passage. Whole genome sequencing was used to identify mutations that cause the distinct morphotypes of ST11 CRKP. Transmission electron microscopy, antimicrobial susceptibility tests, growth assays, biofilm formation, virulence assays, membrane permeability assays, and RNA-seq analysis were used to investigate the specific characteristics associated with different morphotypes of ST11 CRKP. Compared with the parental mcsw morphotype, the ntrd morphotype resulted from mutation of genes involved in capsular polysaccharide biosynthesis (wza, wzc, and wbaP), a result validated by gene knockout experiments. This morphotype showed capsule deficiency and lower virulence potential, but higher biofilm production. By contrast, the msdw morphotype displayed competition deficiency and increased susceptibility to chlorhexidine and polymyxin B. Further analyses indicated that these characteristics were caused by interruption of the sigma factor gene rpoN by insertion mutations and deletion of the rpoN gene, which attenuated membrane integrity presumably by downregulating the phage shock protein operon. These data expand current understanding of genetic, virulence, and antimicrobial resistance characteristics associated with distinct morphotypes in ST11 CRKP.

Therapeutic effect of Wendan Decoction combined with mosapride on gastroesophageal reflux disease after esophageal cancer surgery.

BACKGROUND: Gastroesophageal reflux disease (GERD) is a common complication of esophageal cancer surgery that can affect quality of life and increase the risk of esophageal stricture and anastomotic leakage. Wendan Decoction (WDD) is a traditional Chinese herbal formula used to treat various gastrointestinal disorders, such as gastritis, functional dyspepsia, and irritable bowel syndrome. Mosapride, a prokinetic agent, functions as a selective 5-hydroxytryptamine 4 agonist, enhancing gastrointestinal motility. AIM: To evaluate the therapeutic effects of WDD combined with mosapride on GERD after esophageal cancer surgery. METHODS: Eighty patients with GERD were randomly divided into treatment (receiving WDD combined with mosapride) and control (receiving mosapride alone) groups. The treatment was conducted from January 2021 to January 2023. The primary outcome was improved GERD symptoms as measured using the reflux disease questionnaire (RDQ). The secondary outcomes were improved esophageal motility (measured using esophageal manometry), gastric emptying (measured using gastric scintigraphy), and quality of life [measured via the Short Form-36 (SF-36) Health Survey]. RESULTS: The treatment group showed a notably reduced RDQ score and improved esophageal motility parameters, such as lower esophageal sphincter pressure, peristaltic amplitude, and peristaltic velocity compared to the control group. The treatment group showed significantly higher gastric emptying rates and SF-36 scores (in both physical and mental domains) compared to the control group. No serious adverse effects were observed in either group. CONCLUSION: WDD combined with mosapride is an effective and safe therapy for GERD after esophageal cancer surgery. It can improve GERD symptoms, esophageal motility, gastric emptying, and the quality of life of patients. Further studies with larger sample sizes and longer follow-up periods are required to confirm these findings.

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.

Objective: To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention. Methods: We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance. Results: Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal. Conclusion: Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

Seroprevalence and Risk Factors of Toxoplasma gondii Infection of Cattle and Goats in Farms of Hunan Province, China.

Background: Toxoplasmosis is one of the most widespread foodborne parasitic zoonoses caused by the obligate intracellular protozoan Toxoplasma gondii. Although a number of studies have reported on the seroprevalence and risk factors of T. gondii infection in ruminants in China, information about T. gondii infection in cattle in Hunan province of China is not available. Material and Methods: Sera of 985 cattle and 1147 goats were examined for the presence of specific antibodies against T. gondii using the indirect hemagglutination test. Some risk factors related to the presence of cats, herd size, gender, age, and geographical origin were determined using a binary logistic regression. Results: Specific IgG against T. gondii were detected in 8.3% (82/985) and 13.3% (153/1147) of the cattle and goats, respectively. Based on statistical analysis, the presence of cats and gender were considered important risk factors for T. gondii infection in cattle and goats in the farms in this study (P < 0.05). Conclusion: Our results provide a baseline for future prevention and control of T. gondii infection in cattle and goats in Hunan province, subtropical China. This is the first report of T. gondii seroprevalence in cattle in Hunan province, China.

Value of orthogonal axial MR images in preoperative T staging of gastric cancer.

PURPOSE: To assess the value of orthogonal axial images (OAI) of MRI in gastric cancer T staging. METHODS: This retrospective study enrolled 133 patients (median age, 63 [range, 24-85] years) with gastric adenocarcinoma who underwent both CT and MRI followed by surgery. MRI lacking or incorporating OAI and CT images were evaluated, respectively. Diagnostic performance (accuracy, sensitivity, and specificity) for each T stage, overall diagnostic accuracy and rates of over- and understaging were quantified employing pathological T stage as a reference standard. The McNemar's test was performed to compare the overall accuracy. RESULTS: Among patients with pT1-pT4 disease, MRI with OAI (accuracy: 88.7-94.7%, sensitivity: 66.7-93.0%, specificity: 91.5-100.0%) exhibited superior diagnostic performance compared to MRI without OAI (accuracy: 81.2-88.7%, sensitivity: 46.2-83.1%, specificity: 85.5-99.1%) and CT (accuracy: 88.0-92.5%, sensitivity: 53.3-90.1%, specificity: 88.7-98.1%). The overall accuracy of MRI with OAI was significantly higher (83.5%) than that of MRI without OAI (67.7%) (p < .001). However, there was no significant difference in the overall accuracy of MRI with OAI and CT (78.9%) (p = .35). The over- and understaging rates of MRI with OAI (12.0, 4.5%) were lower than those of MRI without OAI (21.8, 10.5%) and CT (12.8, 8.3%). CONCLUSION: OAI play a pivotal role in the T staging of gastric cancer. MRI incorporating OAI demonstrated commendable performance for gastric cancer T-staging, with a slight tendency toward its superiority over CT.

Higher perfusion of rectum carcinoma relative to tumor-free rectal wall: quantification by a new imaging biomarker diffusion-derived vessel density (DDVD).

Background: Diffusion-derived vessel density (DDVD) is a physiological surrogate of the area of micro-vessels per unit tissue area. DDVD is calculated according to: DDVD(b0b5) = Sb0/ROIarea0 - Sb5/ROIarea5, where Sb0 and Sb5 refer to the tissue signal when b is 0 or 5 s/mm2. This study applied DDVD to assess the perfusion of rectal carcinoma (RC). Methods: MRI was performed with a 3.0-T magnet. Diffusion weighted image with b-values of 0, 5 s/mm2 were acquired in 113 patients with non-mucinous RC and 15 patients with mucinous RC. Diffusion-derived vessel density ratio [DDVDr(b0b5)] was DDVD(b0b5) of RC divided by DDVD(b0b5) of tumor-free rectal wall. Results: The median value of the DDVDr(b0b5) for non-mucinous RCs was 1.430, with the majority of RCs showing a higher DDVD than the adjacent tumor-free wall [i.e., with DDVDr(b0b5) >1]. 90.3% (102/113) of non-mucinous RCs were hypervascular, 1.77% (2/113) were iso-vascular, and 7.96% (9/113) were hypovascular. The median value of the DDVDr(b0b5) for mucinous RCs was 1.660. 73.3% (11/15) of mucinous RCs were hypervascular, and 26.7% (4/15) were hypovascular. A trend (P=0.09) was noted that earlier clinical grades non-mucinous RCs had a higher DDVDr(b0b5) than those of the advanced clinical grades (2.245 for grade 0&I, 1.460 for grade II, 1.430 for grade III, 1.130 for grade IV). A non-significant trend was noted with well and moderately differentiated non-mucinous RCs had a higher DDVDr(b0b5)than that of poorly differentiated non-mucinous RCs (median: 1.460 vs. 1.320). A non-significant trend was noted with MRI-detected extramural vascular invasion (mrEMVI) positive non-mucinous RCs had a higher DDVDr(b0b5) than that of mrEMVI negative non-mucinous RCs (1.630 vs. 1.370). Conclusions: DDVD results in this study approximately agree with contrast agent dynamically enhanced CT literature data.

Compound heterozygous B3GALNT2 mutations in a fetus with encephalocele: A case report.

An encephalocele is a congenital malformation characterized by protrusion of the intracranial contents through a cranial defect. We report that a fetus of a pregnant mother who had two consecutive pregnancies with ultrasound-detected encephalocele carried compound heterozygous variants in B3GALNT2 NM_152490.5:c.[1423C > T (p.Gln475Ter)]; [261-2A > G] of maternal and paternal origins, respectively, as confirmed by exome sequencing followed by Sanger sequencing validation. The present case implies that mutations in B3GALNT2, a well-known dystroglycanopathy causative gene, may result in a phenotype of neural tube defect, providing new insights into the clinical spectrum of B3GALNT2-related disorders. Our study may contribute to prenatal screening/diagnosis and genetic counseling of congenital brain malformations.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.