Hsp47 promotes biogenesis of multi-subunit neuroreceptors in the endoplasmic reticulum.

Protein homeostasis (proteostasis) deficiency is an important contributing factor to neurological and metabolic diseases. However, how the proteostasis network orchestrates the folding and assembly of multi-subunit membrane proteins is poorly understood. Previous proteomics studies identified Hsp47 (Gene: SERPINH1), a heat shock protein in the endoplasmic reticulum lumen, as the most enriched interacting chaperone for gamma-aminobutyric type A (GABAA) receptors. Here, we show that Hsp47 enhances the functional surface expression of GABAA receptors in rat neurons and human HEK293T cells. Furthermore, molecular mechanism study demonstrates that Hsp47 acts after BiP (Gene: HSPA5) and preferentially binds the folded conformation of GABAA receptors without inducing the unfolded protein response in HEK293T cells. Therefore, Hsp47 promotes the subunit-subunit interaction, the receptor assembly process, and the anterograde trafficking of GABAA receptors. Overexpressing Hsp47 is sufficient to correct the surface expression and function of epilepsy-associated GABAA receptor variants in HEK293T cells. Hsp47 also promotes the surface trafficking of other Cys-loop receptors, including nicotinic acetylcholine receptors and serotonin type 3 receptors in HEK293T cells. Therefore, in addition to its known function as a collagen chaperone, this work establishes that Hsp47 plays a critical and general role in the maturation of multi-subunit Cys-loop neuroreceptors.

Risk of rituximab-induced hepatitis B flare after antiviral discontinuation in rheumatic patients with chronic hepatitis B virus.

OBJECTVES: Among immunosuppressants, rituximab is most strongly associated with the risk of hepatitis B virus (HBV) reactivation in chronic HBV individuals. Current guidelines recommending antiviral prophylaxis for these patients on rituximab are predominantly based on studies in oncology. However, limited data existed for the precise risk of HBV flares, effectiveness and optimal duration of antiviral prophylaxis in rituximab-treated rheumatic patients, whose immune status and treatment regimen differ significantly from those of oncology patients. Therefore, we aimed to assess the incidence and clinical outcome of HBV reactivation in HBsAg-positive patients receiving rituximab for various autoimmune diseases who discontinue the antiviral agents. METHODS: A retrospective analysis was performed on 95 hepatitis B surface antigen (HBsAg)-positive patients treated with rituximab for autoimmune diseases in a single centre in Taiwan. HBV related hepatitis, defined as alanine aminotransferase (ALT) more than 3 times of baseline level and concurrent HBV reactivation, after anti-viral discontinuation, was the primary endpoint. Factors associated with HBV hepatitis flare and off-antiviral hepatitis flare were also analysed. RESULTS: With nucleos(t)ide analogues (NA) prophylaxis, no hepatitis flares occurred. However, without prophylaxis, 59% had flare (24.5 per 100 person-years) and 8% experienced liver decompensation. Concurrent steroid use was a dose-dependent risk factor for flare. After NA discontinuation, rituximab "retreatment" led to flares in 75% of cases and liver decompensation in 63% of patients. Stopping NAs within one-year post-rituximab, even without further rituximab treatment, resulted in a 38% flare rate. CONCLUSIONS: This study offers the direct evidence for the necessity of universal antiviral prophylaxis in rheumatic patients with chronic HBV receiving rituximab. After NA discontinuation, rituximab "retreatment" led to even higher flare rate and worse outcome. Patients who completed rituximab treatment should also keep antiviral agents for at least one more year to prevent hepatitis flare.

Efficient Sorting of Semiconducting Single-Walled Carbon Nanotubes in Bio-Renewable Solvents Through Main-Chain Engineering of Conjugated Polymers.

Conjugated polymer sorting is recognized as an efficient and scalable method for the selective extraction of semiconducting single-walled carbon nanotubes (s-SWCNTs). However, this process typically requires the use of nonpolar and aromatic solvents as the dispersion medium, which are petroleum-based and carry significant production hazards. Moreover, there is still potential for improving the efficiency of batch purification. Here, this study presents fluorene-based conjugated polymer that integrates diamines containing ethylene glycol chains (ODA) as linkers within the main chain, to effectively extract s-SWCNTs in bio-renewable solvents. The introduction of ODA segments enhances the solubility in bio-renewable solvents, facilitating effective wrapping of s-SWCNTs in polar environments. Additionally, the ODA within the main chain enhances affinity to s-SWCNTs, thereby contributing to increased yields and purity. The polymer achieves a high sorting yield of 55% and a purity of 99.6% in dispersion of s-SWCNTs in 2-Methyltetrahydrofuran. Thin-film transistor arrays fabricated with sorted s-SWCNTs solution through slot-die coating exhibit average charge carrier mobilities of 20-23 cm2 V⁻¹ s⁻¹ and high on/off current ratios exceeding 105 together with high spatial uniformity. This study highlights the viability of bio-renewable solvents in the sorting process, paving the way for the eco-friendly approach to the purification of SWCNTs.

Human-like adrenal features in Chinese tree shrews revealed by multi-omics analysis of adrenal cell populations and steroid synthesis.

The Chinese tree shrew ( Tupaia belangeri chinensis) has emerged as a promising model for investigating adrenal steroid synthesis, but it is unclear whether the same cells produce steroid hormones and whether their production is regulated in the same way as in humans. Here, we comprehensively mapped the cell types and pathways of steroid metabolism in the adrenal gland of Chinese tree shrews using single-cell RNA sequencing, spatial transcriptome analysis, mass spectrometry, and immunohistochemistry. We compared the transcriptomes of various adrenal cell types across tree shrews, humans, macaques, and mice. Results showed that tree shrew adrenal glands expressed many of the same key enzymes for steroid synthesis as humans, including CYP11B2, CYP11B1, CYB5A, and CHGA. Biochemical analysis confirmed the production of aldosterone, cortisol, and dehydroepiandrosterone but not dehydroepiandrosterone sulfate in the tree shrew adrenal glands. Furthermore, genes in adrenal cell types in tree shrews were correlated with genetic risk factors for polycystic ovary syndrome, primary aldosteronism, hypertension, and related disorders in humans based on genome-wide association studies. Overall, this study suggests that the adrenal glands of Chinese tree shrews may consist of closely related cell populations with functional similarity to those of the human adrenal gland. Our comprehensive results (publicly available at http://gxmujyzmolab.cn:16245/scAGMap/) should facilitate the advancement of this animal model for the investigation of adrenal gland disorders.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Diagnostic performance of computed tomography-digital subtraction angiography and conventional magnetic resonance imaging for evaluating the vascularity of osseous spinal tumors.

BACKGROUND AND PURPOSE: Patients with hypervascular spinal tumors may have severe blood loss during tumor resection, which increases the risks of perioperative morbidity and mortality. However, the preoperative evaluation of tumor vascularity may be challenging; moreover, the reliability of the data obtained in conventional preoperative noninvasive imaging is debatable. In this study, we compared conventional magnetic resonance imaging (MRI) and subtraction computed tomography angiography (CTA) in terms of their performance in vascularity evaluation. The catheter digital subtraction angiography (DSA) technique was used as a reference standard. METHODS: This study included 123 consecutive patients with spinal tumor who underwent subtraction CTA, catheter DSA, and subsequent surgery between October 2015 and October 2021. Data regarding qualitative and semiquantitative subtraction CTA parameters and conventional MRI signs were collected for comparison with tumor vascularity graded through catheter DSA. The diagnostic performance of qualitative CTA, quantitative CTA, and conventional MRI in assessing spinal tumor vascularity was analyzed. RESULTS: Qualitative subtraction CTA was the best noninvasive imaging modality in terms of diagnostic performance (area under the receiver operating characteristic curve [AUROC], 0.95). Quantitative CTA was relatively inferior (AUROC, 0.87). MRI results had low reliability (AUROC, 0.51 to 0.59). Intratumoral hemorrhage and prominent foraminal venous plexus were found to be the specific signs for hypervascularity (specificity 93.2%). CONCLUSIONS: Qualitative subtraction CTA offers the highest diagnostic value in evaluating spinal tumor vascularity, compared to quantitative CTA and MRI. Although conventional MRI may not be a reliable approach, certain MRI signs may have high specificity, which may be crucial for assessing spinal tumor vascularity.

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice.

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS: Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION: This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

Low-Intensity Extracorporeal Shock Wave Therapy Ameliorates Detrusor Hyperactivity with Impaired Contractility via Transient Potential Vanilloid Channels: A Rat Model for Ovarian Hormone Deficiency.

This study explores low-intensity extracorporeal shock wave therapy (LiESWT)'s efficacy in alleviating detrusor hyperactivity with impaired contractility (DHIC) induced by ovarian hormone deficiency (OHD) in ovariectomized rats. The rats were categorized into the following four groups: sham group; OVX group, subjected to bilateral ovariectomy (OVX) for 12 months to induce OHD; OVX + SW4 group, underwent OHD for 12 months followed by 4 weeks of weekly LiESWT; and OVX + SW8 group, underwent OHD for 12 months followed by 8 weeks of weekly LiESWT. Cystometrogram studies and voiding behavior tracing were used to identify the symptoms of DHIC. Muscle strip contractility was evaluated through electrical-field, carbachol, ATP, and KCl stimulations. Western blot and immunofluorescence analyses were performed to assess the expressions of various markers related to bladder dysfunction. The OVX rats exhibited significant bladder deterioration and overactivity, alleviated by LiESWT. LiESWT modified transient receptor potential vanilloid (TRPV) channel expression, regulating calcium concentration and enhancing bladder capacity. It also elevated endoplasmic reticulum (ER) stress proteins, influencing ER-related Ca2+ channels and receptors to modulate detrusor muscle contractility. OHD after 12 months led to neuronal degeneration and reduced TRPV1 and TRPV4 channel activation. LiESWT demonstrated potential in enhancing angiogenic remodeling, neurogenesis, and receptor response, ameliorating DHIC via TRPV channels and cellular signaling in the OHD-induced DHIC rat model.

Investigating a Machine Learning Approach to Predicting White Pixel Defects in Wafers-A Case Study of Wafer Fabrication Plant F.

CMOS image sensor (CIS) semiconductor products are integral to mobile phones and photographic devices, necessitating ongoing enhancements in efficiency and quality for superior photographic outcomes. The presence of white pixels serves as a crucial metric for assessing CIS product performance, primarily arising from metal impurity contamination during the wafer production process or from defects introduced by the grinding blade process. While immediately addressing metal impurity contamination during production presents challenges, refining the handling of defects attributed to grinding blade processing can notably mitigate white pixel issues in CIS products. This study zeroes in on silicon wafer manufacturers in Taiwan, analyzing white pixel defects reported by customers and leveraging machine learning to pinpoint and predict key factors leading to white pixel defects from grinding blade operations. Such pioneering practical studies are rare. The findings reveal that the classification and regression tree (CART) and random forest (RF) models deliver the most accurate predictions (95.18%) of white pixel defects caused by grinding blade operations in a default parameter setting. The analysis further elucidates critical factors like grinding load and torque, vital for the genesis of white pixel defects. The insights garnered from this study aim to arm operators with proactive measures to diminish the potential for customer complaints.

NTRK3 exhibits a pro-oncogenic function in upper tract urothelial carcinomas.

Neurotrophic receptor tyrosine kinase 3 (NTRK3) has pleiotropic functions: it acts not only as an oncogene in breast and gastric cancers but also as a dependence receptor in tumor suppressor genes in colon cancer and neuroblastomas. However, the role of NTRK3 in upper tract urothelial carcinoma (UTUC) is not well documented. This study investigated the association between NTRK3 expression and outcomes in UTUC patients and validated the results in tests on UTUC cell lines. A total of 118 UTUC cancer tissue samples were examined to evaluate the expression of NTRK3. Survival curves were generated using Kaplan-Meier estimates, and Cox regression models were used for investigating survival outcomes. Higher NTRK3 expression was correlated with worse progression-free survival, cancer-specific survival, and overall survival. Moreover, the results of an Ingenuity Pathway Analysis suggested that NTRK3 may interact with the PI3K-AKT-mTOR signaling pathway to promote cancer. NTRK3 downregulation in BFTC909 cells through shRNA reduced cellular migration, invasion, and activity in the AKT-mTOR pathway. Furthermore, the overexpression of NTRK3 in UM-UC-14 cells promoted AKT-mTOR pathway activity, cellular migration, and cell invasion. From these observations, we concluded that NTRK3 may contribute to aggressive behaviors in UTUC by facilitating cell migration and invasion through its interaction with the AKT-mTOR pathway and the expression of NTRK3 is a potential predictor of clinical outcomes in cases of UTUC.

Onco-Circuit Addiction and Onco-Nutrient mTORC1 Signaling Vulnerability in a Model of Aggressive T Cell Malignancy.

How genetic lesions drive cell transformation and whether they can be circumvented without compromising function of non-transformed cells are enduring questions in oncology. Here we show that in mature T cells-in which physiologic clonal proliferation is a cardinal feature- constitutive MYC transcription and Tsc1 loss in mice modeled aggressive human malignancy by reinforcing each other's oncogenic programs. This cooperation was supported by MYC-induced large neutral amino acid transporter chaperone SLC3A2 and dietary leucine, which in synergy with Tsc1 deletion overstimulated mTORC1 to promote mitochondrial fitness and MYC protein overexpression in a positive feedback circuit. A low leucine diet was therapeutic even in late-stage disease but did not hinder T cell immunity to infectious challenge, nor impede T cell transformation driven by constitutive nutrient mTORC1 signaling via Depdc5 loss. Thus, mTORC1 signaling hypersensitivity to leucine as an onco-nutrient enables an onco-circuit, decoupling pathologic from physiologic utilization of nutrient acquisition pathways.

CTLA-4 haplotype predicts HBsAg and HBcrAg levels and HBeAg seroconversion age in children with chronic HBV infection.

Background & Aim: Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) attenuates cytotoxic T lymphocyte (CTL) activation. This study was performed to examine the relationships between CTLA-4 genotypes/haplotypes, hepatitis B surface antigen (HBsAg), and hepatitis B core-related antigen (HBcrAg) levels, and their potential impact on the clinical course of chronic HBV infection. Methods: We recruited 145 treatment-naïve patients with genotype B or C chronic HBV infection who were initially hepatitis B e-antigen (HBeAg)-positive and had been followed from a mean age of 7.08 years for a total of 4,787 person-years in the study cohort. We also recruited another 69 treatment-naïve adults with genotype B or C chronic HBV infection as a validation cohort. We assessed the CTLA-4 gene single nucleotide polymorphisms rs4553808 (-A1661G)/rs5742909 (-C318T) in both cohorts, and the serum HBsAg and HBcrAg levels in the study cohort. Results: CTLA-4 promoter haplotypes were associated with HBsAg and HBcrAg levels at 10 and 15 years of age in the study cohort. Patients with the CTLA-4 AA/CC haplotype showed earlier spontaneous HBeAg seroconversion (hazard ratio = 1.58; p = 0.02), and a more rapid annual decline in the serum HBsAg level than other patients (0.09 vs. 0.03 log10 IU/ml/year, p = 0.02). The CTLA-4 AA/CC haplotype was also predictive of HBeAg seroconversion in the validation cohort (p = 0.01). Conclusions: Chronic HBV-infected patients with a CTLA-4 AA/CC haplotype had lower serum HBsAg and HBcrAg levels in childhood and earlier spontaneous HBeAg seroconversion. Impact and implications: The role of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) in chronic HBV-infected children has not been studied previously. In a very long-term cohort followed from childhood to adulthood, we showed that CTLA-4 haplotypes are associated with HBV biomarker levels in childhood and are correlated with the clinical course of chronic HBV infection. CTLA-4 pathway may serve as a future target for the development of therapeutic agents against HBV infection.

Anticoagulation for Patients With Concomitant Atrial Fibrillation and End-Stage Renal Disease: A Systematic Review and Network Meta-Analysis.

BACKGROUND: Concomitant atrial fibrillation and end-stage renal disease is common and associated with an unfavorable prognosis. Although oral anticoagulants have been well established to prevent thromboembolism, the applicability in patients under long-term dialysis remains debatable. The study aimed to determine the efficacy and safety of anticoagulation in the dialysis-dependent population. METHODS AND RESULTS: An updated network meta-analysis based on MEDLINE, EMBASE, and the Cochrane Library was performed. Studies published up to December 2022 were included. Direct oral anticoagulants (DOACs, dabigatran, rivaroxaban, apixaban 2.5/5 mg twice daily), vitamin K antagonists (VKAs), and no anticoagulation were compared on safety and efficacy outcomes. The outcomes of interest were major bleeding, thromboembolism, and all-cause death. A total of 42 studies, including 3 randomized controlled trials, with 185 864 subjects were pooled. VKAs were associated with a significantly higher risk of major bleeding than either no anticoagulation (hazard ratio [HR], 1.47; 95% CI, 1.34-1.61) or DOACs (DOACs versus VKAs; HR, 0.74 [95% CI, 0.64-0.84]). For the prevention of thromboembolism, the efficacies of VKAs, DOACs, and no anticoagulation were equivalent. Nevertheless, dabigatran and rivaroxaban were associated with fewer embolic events. There were no differences in all-cause death with the administration of VKAs, DOACs, or no anticoagulation. CONCLUSIONS: For dialysis-dependent populations, dabigatran and rivaroxaban were associated with better efficacy, while dabigatran and apixaban demonstrated better safety. No anticoagulation was a noninferior alterative, and VKAs were associated with the worst outcomes.

A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography.

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18-45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model. The proposed structure-focused weighted orientation preprocessing method is composed of the original image, the architecture enhancement map, and the weighted orientation map, highlighting suspicious AD locations. The proposed structure fusion attention model captures the information from different channels and outperforms other models in terms of false positives and top sensitivity, which refers to the maximum sensitivity that a model can achieve under the acceptance of the highest number of false positives, reaching 0.92 top sensitivity with only 0.6590 false positive per image. The findings suggest that the combination of preprocessing methods and a novel network architecture can lead to more accurate and reliable AD detection. Overall, the proposed approach offers a novel perspective on detecting ADs, and we believe that our method can be applied to clinical settings in the future, assisting radiologists in the early detection of ADs from mammography, ultimately leading to early treatment of breast cancer patients.

Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.

Adjuvant chemotherapy in stage 1 colon cancer: Patient characteristics and survival analysis from the national cancer database.

BACKGROUND: A subset of patients in ACS-NCDB with stage-1 colon cancer received adjuvant chemotherapy (AC), in contrast to national guidelines. This study aimed to define this population and evaluate associations between AC and survival. METHODS: Patients with T1-2N0 colon cancer from 2004 to 2016 were separated into AC and non-AC groups. Adverse pathological features (APF) included T2, poor differentiation, lymphovascular invasion, positive margin, and inadequate lymph nodes (<12). Cox proportional hazard models were used to estimate prognostic factors for overall survival (OS). RESULTS: A total of 1745 of 139,857 patients (1.2 %) received AC. Receiving AC was associated with male sex (p = 0.02), uninsured (p < 0.01), low income (p = 0.02), or having ≥2 APFs (p < 0.001). In the total cohort, AC was associated with increased mortality (HR 1.14 [1.04-1.24] P < 0.01). On subset analysis, AC was associated with improved OS for patients with ≥2 APFs (log-rank P=<0.001), and decreased mortality when adjusted for covariates (HR 0.81 [0.69-0.95] P=<0.01). The most significant predictor of mortality was old age (HR 3.78 [3.67, 3.89] p ≤ 0.01), followed by higher Charlson Comorbidity Index (HR 1.73 [1.69, 1.76] (p ≤ 0.01), and higher APF score (HR 1.46 [1.42, 15.2] p ≤ 0.01). CONCLUSION: AC was associated with decreased survival in the total cohort of stage 1 colon cancer patients, but was associated with improved survival for patients with multiple APFs.