Predictors of acute kidney injury in dengue patients: a systematic review and meta-analysis.

BACKGROUND: Dengue infection poses a significant global health challenge, particularly in tropical and subtropical regions. Among its severe complications, Acute kidney injury (AKI) stands out due to its association with increased morbidity, mortality, and healthcare burdens. This Meta-analysis aim to identify and evaluate the predictors of AKI among dengue patients, facilitating early detection and management strategies to mitigate AKI's impact. METHODS: We searched PubMed, EMBASE, and Web of Science databases, covering literature up to February 2024. We included human observational studies reporting on AKI predictors in confirmed dengue cases. Nested-Knowledge software was used for screening and data extraction. The Newcastle-Ottawa Scale was used for quality assessment. R software (V 4.3) was utilized to compute pooled odds ratios (ORs) and 95% confidence intervals (CIs) for each predictor. RESULTS: Our search yielded nine studies involving diverse geographic locations and patient demographics. A total of 9,198 patients were included in the studies, with 542 diagnosed with AKI. in which key predictors of AKI identified include severe forms of dengue (OR: 2.22, 95% CI: 1.02-3.42), male gender (OR: 3.13, 95% CI: 1.82-4.44), comorbidities such as diabetes mellitus (OR: 3.298, 95% CI: 0.274-6.322), and chronic kidney disease (OR: 2.2, 95% CI: 0.42-11.24), as well as co-infections and clinical manifestations like rhabdomyolysis and major bleeding. CONCLUSION: Our study identifies several predictors of AKI in dengue patients. These findings indicate the importance of early identification and intervention for high-risk individuals. Future research should focus on standardizing AKI diagnostic criteria within the dengue context and exploring the mechanisms underlying these associations to improve patient care and outcomes.

Association of electronic cigarette use and suicidal behaviors: a systematic review and meta-analysis.

BACKGROUND: The proliferation of electronic cigarettes (e-cigarettes) has presented new challenges in public health, particularly among adolescents and young adults. While marketed as safer than tobacco and as cessation aids, e-cigarettes have raised concerns about their long-term health and psychosocial impacts, including potential links to increased suicidal behaviors. This study aims to evaluate the relationship between e-cigarette use and suicidal behaviors by conducting a systematic review of the current literature. METHODS: We searched PubMed, Web of Science, and EMBASE for studies up to March 10, 2024, examining the relationship between e-cigarette use and suicidal behaviors. Eligible studies included cross-sectional, longitudinal, retrospective, prospective, and case-control designs. Meta-analysis was performed to calculate pooled odds ratios (ORs). Newcastle Ottawa scale was used to assess the quality of studies. R software (V 4.3) was used to perform the meta-analysis. RESULTS: Our analysis included fourteen studies, predominantly from the US and Korea, with participants ranging from 1,151 to 255,887. The meta-analysis identified a significant association between e-cigarette use and an increased risk of suicidal ideation (OR = 1.489, 95% CI: 1.357 to 1.621), suicide attempts (OR = 2.497, 95% CI: 1.999 to 3.996), and suicidal planning (OR = 2.310, 95% CI: 1.810 to 2.810). Heterogeneity was noted among the studies. CONCLUSION: E-cigarette use is significantly associated with the risk of suicidal behaviors, particularly among adolescents. The findings underscore the necessity for caution in endorsing e-cigarettes as a safer smoking alternative and call for more extensive research to understand the underlying mechanisms. Public health strategies should be developed to address and mitigate the risks of suicidal behaviors among e-cigarette users.

Effectiveness of early Anakinra on cardiac function in children with multisystem inflammatory syndrome of COVID-19: a systematic review.

BACKGROUND: Multisystem Inflammatory Syndrome in Children (MIS-C) associated with SARS-CoV-2 can lead to severe cardiovascular complications. Anakinra, an interleukin-1 receptor antagonist, is proposed to benefit the hyperinflammatory state of MIS-C, potentially improving cardiac function. This systematic review evaluated the effectiveness of early Anakinra administration on cardiac outcomes in children with MIS-C. METHODS: A comprehensive search across PubMed, Embase, and Web of Science until March 2024 identified studies using Anakinra to treat MIS-C with reported cardiac outcomes. Observational cohorts and clinical trials were included, with data extraction focusing on cardiac function metrics and inflammatory markers. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Six studies met the inclusion criteria, ranging from retrospective cohorts to prospective clinical studies, predominantly from the USA. Anakinra dosages ranged from 2.3 to 10 mg/kg based on disease severity. Several studies showed significant improvements in left ventricular ejection fraction and reductions in inflammatory markers like C-reactive protein, suggesting Anakinra's role in enhancing cardiac function and mitigating inflammation. However, findings on vasoactive support needs were mixed, and some studies did not report significant changes in acute cardiac support requirements. CONCLUSION: Early Anakinra administration shows potential for improving cardiac function and reducing inflammation in children with MIS-C, particularly those with severe manifestations. However, the existing evidence is limited by the observational nature of most studies and lacks randomized controlled trials (RCTs). Further high-quality RCTs are necessary to conclusively determine Anakinra's effectiveness and optimize its use in MIS-C management for better long-term cardiac outcomes and standardized treatment protocols.

Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.

Phenotypic clusters of type 2 diabetes mellitus among North Indians reveal higher levels of insulin deficiency along with insulin resistance.

BACKGROUND: Type 2 diabetes is now considered a heterogenous disease. Distinct clusters have been identified with patterns varying between Europeans and South Asians as well as between South Indians who have described a novel cluster; Combined Insulin-Resistant and Deficient Diabetes, and individuals from West and East India who have reported that insulin deficiency is the primary driver of heterogeneity. Therefore, North Indian patients may also have a distinct, novel clustering pattern due to unique genetic, epigenetic, and environmental factors. We aim to identify clusters of type 2 diabetes in North Indians and to describe the different characteristics of these clusters. METHODS: The K value for the optimal number of clusters was obtained from two-step clustering. K means clustering was done with this K value using SPSS 29.0 software. Variables used for clustering were age, BMI, HbA1c, HOMA-beta, HOMA-IR, and waist circumference. RESULTS: Four phenotypically different clusters were identified in 469 individuals with type 2 diabetes. Cluster 1 was severe insulin deficient diabetes (15%), Cluster 2 was severe insulin resistant diabetes (22%), Cluster 3 was moderate obesity-related diabetes (35%), and Cluster 4 was moderate age-related diabetes (27%). Clusters 1 and 2 were similar to earlier studies but in different proportions. Clusters 3 and 4 characteristics were different from earlier studies, with greater impairment in beta cell function and higher HbA1c levels. Significant insulin resistance was noted in all clusters. CONCLUSION: The phenotypic clusters of type 2 diabetes identified in the present study were characterized by high levels of insulin deficiency along with important contributions from insulin resistance.

mtx-COBRA: Subcellular localization prediction for bacterial proteins.

BACKGROUND: Bacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and can induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets. METHOD: We present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Meta's protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset. RESULTS: Using benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb. CONCLUSIONS: mtx-COBRA provides an accessible pipeline that can more efficiently classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.

Establishing the validity of a diagnostic questionnaire for childbirth-related posttraumatic stress disorder.

BACKGROUND: Labor and delivery can entail complications and severe maternal morbidities that threaten a woman's life or cause her to believe that her life is in danger. Women with these experiences are at risk for developing posttraumatic stress disorder. Postpartum posttraumatic stress disorder, or childbirth-related posttraumatic stress disorder, can become an enduring and debilitating condition. At present, validated tools for a rapid and efficient screen for childbirth-related posttraumatic stress disorder are lacking. OBJECTIVE: We examined the diagnostic validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, for detecting posttraumatic stress disorder among women who have had a traumatic childbirth. This Checklist assesses the 20 Diagnostic and Statistical Manual of Mental Disorders, posttraumatic stress disorder symptoms and is a commonly used patient-administrated screening instrument. Its diagnostic accuracy for detecting childbirth-related posttraumatic stress disorder is unknown. STUDY DESIGN: The sample included 59 patients who reported a traumatic childbirth experience determined in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, posttraumatic stress disorder criterion A for exposure involving a threat or potential threat to the life of the mother or infant, experienced or perceived, or physical injury. The majority (66%) of the participants were less than 1 year postpartum (for full sample: median, 4.67 months; mean, 1.5 years) and were recruited via the Mass General Brigham's online platform, during the postpartum unit hospitalization or after discharge. Patients were instructed to complete the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, concerning posttraumatic stress disorder symptoms related to childbirth. Other comorbid conditions (ie, depression and anxiety) were also assessed. They also underwent a clinician interview for posttraumatic stress disorder using the gold-standard Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A second administration of the checklist was performed in a subgroup (n=43), altogether allowing an assessment of internal consistency, test-retest reliability, and convergent and diagnostic validity of the Checklist. The diagnostic accuracy of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, in reference to the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, was determined using the area under the receiver operating characteristic curve; an optimal cutoff score was identified using the Youden's J index. RESULTS: One-third of the sample (35.59%) met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for a posttraumatic stress disorder diagnosis stemming from childbirth. The Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, symptom severity score was strongly correlated with the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, total score (ρ=0.82; P<.001). The area under the receiver operating characteristic curve was 0.93 (95% confidence interval, 0.87-0.99), indicating excellent diagnostic performance of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A cutoff value of 28 maximized the sensitivity (0.81) and specificity (0.90) and correctly diagnosed 86% of women. A higher value (32) identified individuals with more severe posttraumatic stress disorder symptoms (specificity, 0.95), but with lower sensitivity (0.62). Checklist scores were also stable over time (intraclass correlation coefficient, 0.73), indicating good test-retest reliability. Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, scores were moderately correlated with the depression and anxiety symptom scores (Edinburgh Postnatal Depression Scale: ρ=0.58; P<.001 and the Brief Symptom Inventory, anxiety subscale: ρ=0.51; P<.001). CONCLUSION: This study demonstrates the validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, as a screening tool for posttraumatic stress disorder among women who had a traumatic childbirth experience. The instrument may facilitate screening for childbirth-related posttraumatic stress disorder on a large scale and help identify women who might benefit from further diagnostics and services. Replication of the findings in larger, postpartum samples is needed.

Assessment of understanding about human papilloma virus vaccination among undergraduate medical students in a developing country: Perspective from India.

BACKGROUND AND AIMS: Human papilloma virus (HPV) infection is the most common sexually transmitted infection responsible for cervical cancer in women. There is no cure for HPV but safe and effective vaccinations before sexual debut can definitely decrease the incidence of cervical cancer. This research aims to explore the basic understanding of medical students about cervical cancer, HPV and HPV vaccination. METHODS AND MATERIAL: This was a descriptive, questionnaire based cross-sectional study conducted among the undergraduate medical students of All India Institute of Medical Sciences, Jodhpur from April 2018 to May 2018. A total of 238 respondents participated in the study. For statistical analysis, 'Z' score was used for categorical data and student t test was used for normally distributed continuous data. RESULTS: Overall, 41% students had good knowledge about HPV infection and HPV vaccination while 44% students had average knowledge and 15% had poor knowledge. The majority of them (>80%) knew that HPV is responsible for cervical cancer and ano-genital warts but their awareness was not of the same order when it came to associating HPV with penile and oropharyngeal cancer (60%). Females had better knowledge as compared to males and this difference was statistically significant (P < 0.05). 88% of the students were willing to accept the vaccination while only 10% of females were previously vaccinated. CONCLUSION: Medical students, who are potential recipients of the HPV vaccine themselves, can play a unique role in promoting awareness about HPV vaccination in the future.