Safety and efficacy of biliary suprapapillary metal and plastic stents in malignant biliary obstruction: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Biliary drainage is vital in managing malignant biliary obstruction (MBO). Suprapapillary stenting has emerged as a viable alternative to transpapillary stenting and is performed using inside plastic (iPS) or metal stents (iMS). This meta-analysis aims to evaluate the outcomes of suprapapillary stent placement for MBO. METHODS: The Embase, PubMed, and Web of Science databases were systematically searched to include all studies published before September 31, 2023, that reported on the outcomes of suprapapillary stents placed for MBO. Using the random-effect model, the pooled, weight-adjusted event rate estimate for the clinical outcomes was calculated with 95% confidence intervals (CIs). RESULTS: Twenty-eight studies were included, with a total of 1401 patients. The pooled clinical success rate was 98.9%. A subgroup analysis yielded non-significant differences between the iPS and iMS groups (99.3% vs. 98.6%, respectively; P = 0.44). The pooled incidence rate of adverse events (AE) with suprapapillary stents was 9.5%. In a subgroup analysis, the incidence of AEs with iPS was 10.7% compared to 9% in the iMS group without a statistical difference (P = 0.32). The most common adverse event was cholangitis (2.2%), followed by pancreatitis (1.1%), cholecystitis (0.5%), and bleeding (0.12%). CONCLUSION: When technically feasible, suprapapillary stenting for MBO is a viable endoscopic option with a high clinical success rate and acceptable adverse event rates. Both iPS and iMS exhibit similar efficacy.

Needle-Knife Fistulotomy Versus Needle-Knife Papillotomy in Difficult Biliary Cannulation: A Systematic Review and Meta-Analysis.

Background: Endoscopic retrograde cholangiopancreatography (ERCP) is an essential endoscopic therapeutic modality for biliary and pancreatic diseases. Needle-knife fistulotomy (NKF) and papillotomy (NKP) are the two most commonly used rescue techniques for patients with difficult biliary cannulation. However, there remains a need for comparative studies on these approaches to inform clinical decision-making. This meta-analysis aimed to evaluate the efficacy and safety of NKF compared to NKP as a rescue technique in difficult biliary cannulation after failed conventional ERCP. Methods: We searched PubMed, Scopus, Embase, and Web of Science databases through November 2023 to include all studies that directly compared the outcomes of NKF with NKP in difficult biliary cannulation. Single-arm studies were excluded. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for dichotomous data related to clinical events were calculated using the Mantel-Haenszel method within a random-effect model. The primary outcome was the biliary cannulation success rate. Results: Four studies with 823 patients (n = 376 NKF vs. n = 447 NKP) were included in our analysis. There was no significant difference between the two groups in biliary cannulation success rate (91.7% vs. 86.9%, respectively; OR = 1.54, 95% CI: 0.21 - 2.49, P = 0.14; I2 = 0%). However, the overall rate of adverse events was significantly lower in the NKF group than in the NKP group (OR = 0.46, 95% CI: 0.25 - 0.84, P = 0.01). Pancreatitis (OR = 0.23, 95% CI: 0.05 - 1.11, P = 0.07) and bleeding (OR = 1.43, 95% CI: 0.59 - 3.46, P = 0.42) were similar between the two groups. No significant differences in cholangitis, cholecystitis, perforation, or mortality were observed. Conclusions: Our meta-analysis indicates comparable success rates in comparing NKF and NKP techniques for difficult biliary cannulation after failed conventional ERCP cannulation. Notably, the NKF technique significantly reduces overall adverse events compared to NKP, suggesting that NKF may be preferable due to its favorable safety profile. Additional randomized controlled trials (RCTs) are warranted to evaluate the interval benefit of an NKF technique.

Semantic segmentation of microbial alterations based on SegFormer.

Introduction: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation. Results: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general. Discussion: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.

Unlocking the Therapeutic Potential of BCL-2 Associated Protein Family: Exploring BCL-2 Inhibitors in Cancer Therapy.

Apoptosis, programmed cell death pathway, is a vital physiological mechanism that ensures cellular homeostasis and overall cellular well-being. In the context of cancer, where evasion of apoptosis is a hallmark, the overexpression of anti-apoptotic proteins like Bcl2, Bcl-xL and Mcl-1 has been documented. Consequently, these proteins have emerged as promising targets for therapeutic interventions. The BCL-2 protein family is central to apoptosis and plays a significant importance in determining cellular fate serving as a critical determinant in this biological process. This review offers a comprehensive exploration of the BCL-2 protein family, emphasizing its dual nature. Specifically, certain members of this family promote cell survival (known as anti-apoptotic proteins), while others are involved in facilitating cell death (referred to as pro-apoptotic and BH3-only proteins). The potential of directly targeting these proteins is examined, particularly due to their involvement in conferring resistance to traditional cancer therapies. The effectiveness of such targeting strategies is also discussed, considering the tumor's propensity for anti-apoptotic pathways. Furthermore, the review highlights emerging research on combination therapies, where BCL-2 inhibitors are used synergistically with other treatments to enhance therapeutic outcomes. By understanding and manipulating the BCL-2 family and its associated pathways, we open doors to innovative and more effective cancer treatments, offering hope for resistant and aggressive cases.

COVID-19 booster enhances IgG mediated viral neutralization by human milk in vitro.

Background: Facilitated by the inability to vaccinate, and an immature immune system, COVID-19 remains a leading cause of death among children. Vaccinated lactating mothers produce specific SARS-CoV-2 antibodies in their milk, capable of neutralizing the virus in vitro. Our objective for this study is to assess the effect of COVID-19 booster dose on SARS-CoV-2 antibody concentration and viral neutralization in milk, plasma, and infant stool. Methods: Thirty-nine mothers and 25 infants were enrolled from December 2020 to May 2022. Milk, maternal plasma, and infants' stool were collected at various time-points up to 12 months following mRNA COVID-19 vaccination. A subgroup of 14 mothers received a booster dose. SARS-CoV-2 antibody levels and their neutralization capacities were assessed. Results: Booster vaccination led to significantly higher IgG levels within human milk and breastfed infants' stool. In vitro neutralization of VSV-gfp-SARS-CoV-2-S-gp, a laboratory safe SARS-CoV-2 like pseudovirus, improved following the booster, with a 90% increase in plasma neutralization and a 60% increase in milk neutralization. We found that post-booster neutralization by human milk was highly correlated to SARS-CoV-2 IgG level. In support of our correlation result, Protein G column depletion of IgG in milk yielded a significant reduction in viral neutralization (p = 0.04). Discussion: The substantial increase in neutralizing IgG levels in milk and breastfed infants' stool post-booster, coupled with the decrease in milk neutralization capabilities upon IgG depletion, underscores the efficacy of booster doses in augmenting the immune response against SARS-CoV-2 in human milk.

The clinical and genetic landscape of developmental and epileptic encephalopathies in Egyptian children.

Developmental and epileptic encephalopathies (DEEs) are a heterogeneous group of epilepsies characterized by early-onset, refractory seizures associated with developmental regression or impairment, with a heterogeneous genetic landscape including genes implicated in various pathways and mechanisms. We retrospectively studied the clinical and genetic data of patients with genetic DEE who presented at two tertiary centers in Egypt over a 10-year period. Exome sequencing was used for genetic testing. We report 74 patients from 63 unrelated Egyptian families, with a high rate of consanguinity (58%). The most common seizure type was generalized tonic-clonic (58%) and multiple seizure types were common (55%). The most common epilepsy syndrome was early infantile DEE (50%). All patients showed variable degrees of developmental impairment. Microcephaly, hypotonia, ophthalmological involvement and neuroimaging abnormalities were common. Eighteen novel variants were identified and the phenotypes of five DEE genes were expanded with novel phenotype-genotype associations. Obtaining a genetic diagnosis had implications on epilepsy management in 17 patients with variants in 12 genes. In this study, we expand the phenotype and genotype spectrum of DEE in a large single ethnic cohort of patients. Reaching a genetic diagnosis guided precision management of epilepsy in a significant proportion of patients.

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Optimizing classification of diseases through language model analysis of symptoms.

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization-Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

Refining the phenotypic spectrum of CCDC88A-related PEHO-like syndrome.

Progressive encephalopathy with edema, hypsarrhythmia, and optic atrophy (PEHO) and PEHO-like syndromes are very rare infantile disorders characterized by profound intellectual disability, hypotonia, convulsions, optic, and progressive brain atrophy. Many causative genes for PEHO and PEHO-like syndromes have been identified including CCDC88A. So far, only five patients from two unrelated families with biallelic CCDC88A variants have been reported in the literature. Herein, we describe a new family from Egypt with a lethal epileptic encephalopathy. Our patient was the youngest child born to a highly consanguineous couple and had a family history of five deceased sibs with the same condition. She presented with postnatal microcephaly, poor visual responsiveness, and epilepsy. Her brain MRI showed abnormal cortical gyration with failure of opercularization of the insula, hypogenesis of corpus callosum, colpocephaly, reduced white matter, hypoplastic vermis, and brain stem. Whole exome sequencing identified a new homozygous frameshift variant in CCDC88A gene (c.1795_1798delACAA, p.Thr599ValfsTer4). Our study presents the third reported family with this extremely rare disorder. We also reviewed all described cases to better refine the phenotypic spectrum associated with biallelic loss of function variants in the CCDC88A gene.

Bi-allelic genetic variants in the translational GTPases GTPBP1 and GTPBP2 cause a distinct identical neurodevelopmental syndrome.

The homologous genes GTPBP1 and GTPBP2 encode GTP-binding proteins 1 and 2, which are involved in ribosomal homeostasis. Pathogenic variants in GTPBP2 were recently shown to be an ultra-rare cause of neurodegenerative or neurodevelopmental disorders (NDDs). Until now, no human phenotype has been linked to GTPBP1. Here, we describe individuals carrying bi-allelic GTPBP1 variants that display an identical phenotype with GTPBP2 and characterize the overall spectrum of GTP-binding protein (1/2)-related disorders. In this study, 20 individuals from 16 families with distinct NDDs and syndromic facial features were investigated by whole-exome (WES) or whole-genome (WGS) sequencing. To assess the functional impact of the identified genetic variants, semi-quantitative PCR, western blot, and ribosome profiling assays were performed in fibroblasts from affected individuals. We also investigated the effect of reducing expression of CG2017, an ortholog of human GTPBP1/2, in the fruit fly Drosophila melanogaster. Individuals with bi-allelic GTPBP1 or GTPBP2 variants presented with microcephaly, profound neurodevelopmental impairment, pathognomonic craniofacial features, and ectodermal defects. Abnormal vision and/or hearing, progressive spasticity, choreoathetoid movements, refractory epilepsy, and brain atrophy were part of the core phenotype of this syndrome. Cell line studies identified a loss-of-function (LoF) impact of the disease-associated variants but no significant abnormalities on ribosome profiling. Reduced expression of CG2017 isoforms was associated with locomotor impairment in Drosophila. In conclusion, bi-allelic GTPBP1 and GTPBP2 LoF variants cause an identical, distinct neurodevelopmental syndrome. Mutant CG2017 knockout flies display motor impairment, highlighting the conserved role for GTP-binding proteins in CNS development across species.

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

Incidence and types of congenital heart disease at a referral hospital in Jordan: retrospective study from a tertiary center.

Background: Congenital heart disease (CHD) is the most common birth defect and accounts for significant global morbidity and mortality. Relatively little is known about the epidemiology of CHD in Jordan or the manner in which CHD is identified. Methods: A retrospective medical record review was conducted for all neonates who had an abnormal echocardiogram performed at a tertiary referral hospital. All included neonates had echocardiography performed by the same pediatric cardiologist at the discretion of the treatment team. Descriptive statistics were used to describe CHD incidence, types of CHD identified, and mechanism of identification. Results: The incidence of congenital heart disease was 17.8 per 1,000 live births. This rose to 24.6 per 1,000 if patent ductus arteriosus in preterm infants was included. The most common identified abnormalities were PDA, atrial septal defects, persistent pulmonary hypertension, septal hypertrophy, and ventricular septal defects. Most children were evaluated either for a murmur heard on exam or as a part of screening due to other comorbidities or risk factors. Less than 1% of children had a prenatal diagnosis. There was a higher rate of persistent pulmonary hypertension during the COVID-19 pandemic than before (p < 0.001). Conclusions: There is a high incidence of CHD in Jordan. Increased prenatal and perinatal screening for CHD may allow for earlier detection.

Lunapark deficiency leads to an autosomal recessive neurodevelopmental phenotype with a degenerative course, epilepsy and distinct brain anomalies.

LNPK encodes a conserved membrane protein that stabilizes the junctions of the tubular endoplasmic reticulum network playing crucial roles in diverse biological functions. Recently, homozygous variants in LNPK were shown to cause a neurodevelopmental disorder (OMIM#618090) in four patients displaying developmental delay, epilepsy and nonspecific brain malformations including corpus callosum hypoplasia and variable impairment of cerebellum. We sought to delineate the molecular and phenotypic spectrum of LNPK-related disorder. Exome or genome sequencing was carried out in 11 families. Thorough clinical and neuroradiological evaluation was performed for all the affected individuals, including review of previously reported patients. We identified 12 distinct homozygous loss-of-function variants in 16 individuals presenting with moderate to profound developmental delay, cognitive impairment, regression, refractory epilepsy and a recognizable neuroimaging pattern consisting of corpus callosum hypoplasia and signal alterations of the forceps minor ('ear-of-the-lynx' sign), variably associated with substantia nigra signal alterations, mild brain atrophy, short midbrain and cerebellar hypoplasia/atrophy. In summary, we define the core phenotype of LNPK-related disorder and expand the list of neurological disorders presenting with the 'ear-of-the-lynx' sign suggesting a possible common underlying mechanism related to endoplasmic reticulum-phagy dysfunction.

Predictors of Surgical Outcomes in Patients with Abducens Nerve Schwannoma: A Comprehensive Systematic Review and a Case Report.

BACKGROUND: Abducens nerve (AN) schwannomas are extremely rare tumors. Clinical characteristics and factors that influence postoperative outcomes are not well defined. OBJECTIVE: To characterize clinical features of AN schwannomas and predictors of surgical outcomes. METHODS: PRISMA-guided systematic review of the literature on AN schwannomas was performed. Subsequently, univariate and multivariate regression analyses were performed to identify the predictive value of variables that influence postoperative outcomes. RESULTS: A total of 42 studies with 55 patients were evaluated. The mean age at presentation was 43.9 ± 14.6 years. The most common presenting symptom was cranial nerve VI palsy (69.1%). Cavernous sinus (49.1%) and prepontine cistern (36.3%) were the most commonly involved locations. Complete recovery after surgery was seen in 36.3% at a median follow-up of 28.4 ± 25.8 months. Preoperative AN palsy (P < 0.001), suboccipital approach (P = 0.007), and subtotal resection of tumor (P = 0.044) were significant protective factors for postoperative complications. Prepontine location and postoperative complications were poor prognostic indicators of AN recovery (odds ratio [OR], 0.10, P = 0.030 and OR, 0.10, P = 0.028, respectively). Subtotal resection was significantly correlated with higher odds of AN recovery (OR, 6.06; P = 0.040). CONCLUSIONS: AN schwannomas are rare but serious tumors that can cause significant morbidity, with only approximately one third of patients showing complete recovery after surgery. The suboccipital approach was a protective factor for postoperative complications, especially when combined with subtotal resection. Knowledge of these factors along with tumor characteristics helps optimize surgical planning and preoperative counseling.

An atypical expression of core α-Dystroglycan and Laminin-α2 in skin fibroblasts of patients with congenital muscular dystrophies.

BACKGROUND: Congenital muscular dystrophies (CMDs) result from genetically inherited defects in the biosynthesis and/or the posttranslational modification (glycosylation) of laminin-α2 and α-dystroglycan (α-DG), respectively. The interaction between both proteins is responsible for the stability and integrity of the muscle cell. We aimed to study the expression profiles of both proteins in two classes of CMDs. SUBJECTS AND METHODS: Whole-exome sequencing (WES) was done for four patients with neuromuscular manifestations. The expression of core α-DG and laminin-α2 subunit in skin fibroblasts and MCF-7 cells was assessed by western blot. RESULTS: WES revealed two cases with nonsense mutations; c.2938G > T and c.4348 C > T, in LAMA2 encodes laminin-α2. It revealed also two cases with mutations in POMGNT1 encode protein O-mannose beta-1,2-N-acetylglucosaminyltransferase mutations. One patient had a missense mutation c.1325G > A, and the other had a synonymous variant c.636 C > T. Immunodetection of core α-DG in skin fibroblasts revealed the expression of truncated forms of core α-DG accompanied by reduced expression of laminin-α2 in POMGNT1-CMD patients and one patient with LAMA2-CMD. One patient with LAMA2-CMD had overexpression of laminin-α2 and expression of a low level of an abnormal form of increased molecular weight core α-DG. MCF-7 cells showed truncated forms of core α-CDG with an absent laminin-α2. CONCLUSION: A correlation between the expression pattern/level of core α-DG and laminin-α2 could be found in patients with different types of CMD.

Breaking Down the Gut: A Case of Severe Toxin-Mediated Colitis.

Shiga-toxin-producing Escherichia coli (STEC) is a worldwide, foodborne pathogen that can lead to life-threatening complications. Transmission has been associated with undercooked meat products, contaminated food and water sources, person-to-person contact, and direct exposure to infected farm animals. As the name suggests, the major virulence factors contributing to its pathogenicity are Shiga toxins, which can cause a spectrum of clinical presentations ranging from mild watery diarrhea to severe hemorrhagic colitis due to its toxic effects on the gastrointestinal tract. We report a case of a 21-year-old man seeking medical attention due to severe crampy abdominal pain and bloody diarrhea who was ultimately diagnosed with a less commonly encountered severe form of colitis in the setting of STEC infection. Thorough investigations while maintaining a high level of clinical suspicion allowed prompt medical care with a complete resolution of symptoms. This case highlights the importance of having high clinical suspicion for STEC even with more severe forms of colitis and sheds light on the role of medical personnel in managing such cases.