Early versus delayed necrosectomy in pancreatic necrosis: A population-based cohort study on readmission, healthcare utilization, and in-hospital mortality.

BACKGROUND: Acute necrotizing pancreatitis is a severe and life-threatening condition. It poses a considerable challenge for clinicians due to its complex nature and the high risk of complications. Several minimally invasive and open necrosectomy procedures have been developed. Despite advancements in treatment modalities, the optimal timing to perform necrosectomy lacks consensus. AIM: To evaluate the impact of necrosectomy timing on patients with pancreatic necrosis in the United States. METHODS: A national retrospective cohort study was conducted using the 2016-2019 Nationwide Readmissions Database. Patients with non-elective admissions for pancreatic necrosis were identified. The participants were divided into two groups based on the necrosectomy timing: The early group received intervention within 48 hours, whereas the delayed group underwent the procedure after 48 hours. The various intervention techniques included endoscopic, percutaneous, or surgical necrosectomy. The major outcomes of interest were 30-day readmission rates, healthcare utilization, and inpatient mortality. RESULTS: A total of 1309 patients with pancreatic necrosis were included. After propensity score matching, 349 cases treated with early necrosectomy were matched to 375 controls who received delayed intervention. The early cohort had a 30-day readmission rate of 8.6% compared to 4.8% in the delayed cohort (P = 0.040). Early necrosectomy had lower rates of mechanical ventilation (2.9% vs 10.9%, P < 0.001), septic shock (8% vs 19.5%, P < 0.001), and in-hospital mortality (1.1% vs 4.3%, P = 0.01). Patients in the early intervention group incurred lower healthcare costs, with median total charges of $52202 compared to $147418 in the delayed group. Participants in the early cohort also had a relatively shorter median length of stay (6 vs 16 days, P < 0.001). The timing of necrosectomy did not significantly influence the risk of 30-day readmission, with a hazard ratio of 0.56 (95% confidence interval: 0.31-1.02, P = 0.06). CONCLUSION: Our findings show that early necrosectomy is associated with better clinical outcomes and lower healthcare costs. Delayed intervention does not significantly alter the risk of 30-day readmission.

Pashto poetry generation: deep learning with pre-trained transformers for low-resource languages.

Generating poetry using machine and deep learning techniques has been a challenging and exciting topic of research in recent years. It has significance in natural language processing and computational linguistics. This study introduces an innovative approach to generate high-quality Pashto poetry by leveraging two pre-trained transformer models, LaMini-Cerebras-590M and bloomz-560m. The models were trained on an extensive new and quality Pashto poetry dataset to learn the underlying complex patterns and structures. The trained models are then used to generate new Pashto poetry by providing them with a seed text or prompt. To evaluate the quality of the generated poetry, we conducted both subjective and objective evaluations, including human evaluation. The experimental results demonstrate that the proposed approach can generate Pashto poetry that is comparable in quality to human-generated poetry. The study provides a valuable contribution to the field of Pashto language and poetry generation and has potential applications in natural language processing and computational linguistics.

Common pediatric surgical conditions and associated health-seeking behaviors in Pakistan: An urban and rural comparative assessment.

Approximately five billion people do not have access to necessary surgical treatment globally and up to 85% of children in LMICs are affected with conditions requiring surgical care by the age of 15 years. It is crucial to identify common surgical conditions in children in Pakistan to inform healthcare professionals and policymakers for effective resource allocation. This representative cross-sectional household survey conducted on children aged 5-10 years assessed existing surgical diseases and healthcare-seeking behaviors in the two largest provinces (Sindh and Punjab) of Pakistan. The data was collected through a validated cross-sectional survey tool [Surgeons OverSeas Assessment of Surgical Need (SOSAS)]. Caregivers were asked about their child's recent and past surgical conditions in six distinct anatomical regions and pictures were taken of identified conditions after appropriate consent for further diagnosis. Health-seeking behaviors including the kind of treatment sought, the nature of care received, and the reasons for not receiving care were noted. 13.5% of children surveyed reported a surgical condition, with a similar distribution across urban (13.2%) and rural (13.7) areas and the most common cause was trauma. The greatest number of surgical conditions were found to be on the head and neck region (57.7%), while the back accounted for the least number of conditions (1.7%). Our results outline a need for organizing all entities (governmental, non-governmental, and private) involved in child health to ensure efficient resource allocation to cater to existing surgical problems.

Advancements in FR4 dielectric analysis: Free space approach and measurement validation.

In this study, the free space approach is utilized to calculate the relative permittivity of FR4 by utilizing the Nicholson-Ross-Weir Conversion. By examining the scattering characteristics, the free space technique offers a practical tool for describing dielectric materials. The simulations were run on CST-2019, and the frequency range of 8.5 GHz to 11.5 GHz was chosen. Experimental measurements were carried out utilizing a Vector Network Analyzer, To further reduce outside influences and assure accurate measurements in a controlled setting, an anechoic chamber was used. The outcomes of the simulations and actual measurements show the significance of the Nicholson- Ross-Weir Conversion and free space approach in calculating the relative permittivity of FR4. The correctness and dependability of the suggested technique are confirmed by the good agreement between the simulated and measured outcomes. This study makes a contribution to the field of electromagnetic characterization and offers a useful method for figuring out FR4's dielectric characteristics. The results of this study have substantial effects on PCB design and optimization as well as other high-frequency electronic devices that operate in the frequency band under consideration.

Is the fire even bigger? Burnout in 800 medical and nursing students in a low middle income country.

BACKGROUND: Burnout, characterized by emotional exhaustion (EX), depersonalization (DP), and a reduced sense of personal efficacy (PF) among medical and nursing students can lead to suicidal ideation, lack of empathy, and dropouts. Previous studies have used over-simplified definitions of burnout that fail to capture its complexity. We describe the prevalence of burnout profiles and its risk factors among medical and nursing students. METHODS: A cross sectional study was conducted at a tertiary care University Hospital in Pakistan. The Maslach Burnout Inventory (MBI) survey was disseminated via SurveyMonkey over a period of 4 months (November 2019 to February 2020) to 482 Medical and 441 nursing students. The MBI tool measures the dimensions of EX, DP, and PF to describe seven burnout profiles. Multivariable regression was used to identify predictors of burnout. RESULTS: The response rate was 92% in nursing and 87.3% in medical students. The prevalence of burnout in medical and nursing students was 16.9% and 6.7% respectively (p<0.001), with 55.7% (n = 427) suffering from at least one burnout profile. Only 32.5% (n = 250) students felt engaged, (42.3% medical, 22.7% nursing students, p<0.001). The most common profile was ineffective (32.5%, n = 250), characterized by a reduced sense of personal efficacy (35.6% medical, 29.4% nursing students; p = 0.065). Medical students were at higher risk of burnout compared to nursing students (OR = 2.49 [1.42, 4.38]; p<0.001) with highest risk observed in year 4 (OR = 2.47 [1.02, 5.99]; p = 0.046). Other risk factors for burnout included occasional drug use (OR = 1.83 [1.21, 8.49]; p = 0.017) and living in a hostel (OR = 1.64 [1.01,2.67]; p = 0.233). CONCLUSION AND RELEVANCE: Two-thirds of our participants experienced at least one dimension of burnout with the highest prevalence of a reduced sense of PF. Drivers of burnout unique to a lower-middle-income country need to be understood for effective interventions. Faculty training on principles of student evaluation and feedback may be beneficial.

Range aware message drop policy for delay tolerant networks.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Aware Drop (RAD) is proposed which considers metrics such as message size and time to live (TTL). RAD utilizes TTL as an important metric and as a result, reduces the unnecessary message drop. Simulation results reveal that RAD performs significantly better than drop oldest (DOA) and size aware drop (SAD) in terms of delivery probability and overhead ratio. The obtained results also revealed that the hop-count average of SAD is 3.9 and DOA is 3.4 while the hop-count average of RAD is just 1.7. Also, the message drop ratio of the RAD is 36.2% while SAD and DOA have message drop ratios of 73.3% and 84.9% respectively.

Depression detection with machine learning of structural and non-structural dual languages.

Depression is a serious mental state that negatively impacts thoughts, feelings, and actions. Social media use is rapidly growing, with people expressing themselves in their regional languages. In Pakistan and India, many people use Roman Urdu on social media. This makes Roman Urdu important for predicting depression in these regions. However, previous studies show no significant contribution in predicting depression through Roman Urdu or in combination with structured languages like English. The study aims to create a Roman Urdu dataset to predict depression risk in dual languages [Roman Urdu (non-structural language) + English (structural language)]. Two datasets were used: Roman Urdu data manually converted from English on Facebook, and English comments from Kaggle. These datasets were merged for the research experiments. Machine learning models, including Support Vector Machine (SVM), Support Vector Machine Radial Basis Function (SVM-RBF), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT), were tested. Depression risk was classified into not depressed, moderate, and severe. Experimental studies show that the SVM achieved the best result with anaccuracy of 0.84% compared to existing models. The presented study refines thearea of depression to predict the depression in Asian countries.

Pashto script and graphics detection in camera captured Pashto document images using deep learning model.

Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%.

Comparison of Health Parameters in Postpartum Diastasis Recti: A Randomized Control Trial of SEMG Biofeedback-Assisted Core Strengthening Exercises with Kinesiotaping vs. Non-Assisted Exercises.

Current medical treatments for diastasis recti often involve exercises to strengthen the core muscles, along with abdominal binders or supports. However, there is limited evidence comparing the effectiveness of surface electromyography (SEMG) biofeedback-assisted core strengthening exercises combined with kinesiotaping to other approaches. This study aimed to assess the impact of three interventions on core strength, inter-rectus distance, and quality of life in postpartum women with diastasis recti. The interventions included core strengthening exercises with kinesiotaping and SEMG biofeedback-assisted core strengthening with kinesiotaping. This randomized controlled trial (NCT05897255) included 24 postpartum women divided into three groups. We measured inter-rectus distance, quality of life using the Short Form Health Survey 36, and core strength using the McGill torso battery test. The SEMG biofeedback provided auditory and visual cues. We used one-way analysis of variance to compare outcomes between groups, while a t-test for within-group analysis. Both the SEMG biofeedback-assisted and non-assisted core strengthening exercises with kinesiotaping groups showed significantly greater improvements in energy, bodily pain, general health, physical functioning, and limitations due to physical problems than the core strengthening group. Additionally, the SEMG biofeedback-assisted group demonstrated a greater reduction in inter-rectus distance. There were no statistically significant differences in core strength improvement among the three groups. Core strengthening exercises with SEMG-assisted kinesiotaping were superior to core strengthening alone in reducing inter-rectus distance, enhancing physical function, energy levels, and general health, and decreasing bodily pain and limitations due to physical problems. Core strength improvements were similar across all groups.

Total RNA analysis of the active microbiome on moving bed biofilm reactor carriers under incrementally increasing micropollutant concentrations.

Micropollutants are increasingly prevalent in the aquatic environment. A major part of these originates from wastewater treatment plants since traditional treatment technologies do not remove micropollutants sufficiently. Moving bed biofilm reactors (MBBRs), however, have been shown to aid in micropollutant removal when applied to conventional wastewater treatment as a polishing step. Here, we used Total RNA sequencing to investigate both the active microbial community and functional dynamics of MBBR biofilms when these were exposed to increasing micropollutant concentrations over time. Concurrently, we conducted batch culture experiments using biofilm carriers from the MBBRs to assess micropollutant degradation potential. Our study showed that biofilm eukaryotes, in particular protozoa, were negatively influenced by micropollutant exposure, in contrast to prokaryotes that increased in relative abundance. Further, we found several functional genes that were differentially expressed between the MBBR with added micropollutants and the control. These include genes involved in aromatic and xenobiotic compound degradation. Moreover, the biofilm carrier batch experiment showed vastly different alterations in benzotriazole and diclofenac degradation following the increased micropollutant concentrations in the MBBR. Ultimately, this study provides essential insights into the microbial community and functional dynamics of MBBRs and how an increased load of micropollutants influences these dynamics.

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

Association between acute peripancreatic fluid collections and early readmission in acute pancreatitis: A propensity-matched analysis.

BACKGROUND: Patients with acute pancreatitis (AP) frequently experience hospital readmissions, posing a significant burden to healthcare systems. Acute peripancreatic fluid collection (APFC) may negatively impact the clinical course of AP. It could worsen symptoms and potentially lead to additional complications. However, clinical evidence regarding the specific association between APFC and early readmission in AP remains scarce. Understanding the link between APFC and readmission may help improve clinical care for AP patients and reduce healthcare costs. AIM: To evaluate the association between APFC and 30-day readmission in patients with AP. METHODS: This retrospective cohort study is based on the Nationwide Readmission Database for 2016-2019. Patients with a primary diagnosis of AP were identified. Participants were categorized into those with and without APFC. A 1:1 propensity score matching for age, gender, and Elixhauser comorbidities was performed. The primary outcome was early readmission rates. Secondary outcomes included the incidence of inpatient complications and healthcare utilization. Unadjusted analyses used Mann-Whitney U and χ 2 tests, while Cox regression models assessed 30-day readmission risks and reported them as adjusted hazard ratios (aHR). Kaplan-Meier curves and log-rank tests verified readmission risks. RESULTS: A total of 673059 patients with the principal diagnosis of AP were included. Of these, 5.1% had APFC on initial admission. After propensity score matching, each cohort consisted of 33914 patients. Those with APFC showed a higher incidence of inpatient complications, including septic shock (3.1% vs 1.3%, P < 0.001), portal venous thrombosis (4.4% vs 0.8%, P < 0.001), and mechanical ventilation (1.8% vs 0.9%, P < 0.001). The length of stay (LOS) was longer for APFC patients [4 (3-7) vs 3 (2-5) days, P < 0.001], as were hospital charges ($29451 vs $24418, P < 0.001). For 30-day readmissions, APFC patients had a higher rate (15.7% vs 6.5%, P < 0.001) and a longer median readmission LOS (4 vs 3 days, P < 0.001). The APFC group also had higher readmission charges ($28282 vs $22865, P < 0.001). The presence of APFC increased the risk of readmission twofold (aHR 2.52, 95% confidence interval: 2.40-2.65, P < 0.001). The independent risk factors for 30-day readmission included female gender, Elixhauser Comorbidity Index ≥ 3, chronic pulmonary diseases, chronic renal disease, protein-calorie malnutrition, substance use disorder, depression, portal and splenic venous thrombosis, and certain endoscopic procedures. CONCLUSION: Developing APFC during index hospitalization for AP is linked to higher readmission rates, more inpatient complications, longer LOS, and increased healthcare costs. Knowing predictors of readmission can help target high-risk patients, reducing healthcare burdens.

Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.

BACKGROUND: Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision-making. Skin lesion segmentation from images is a crucial step toward achieving this goal-timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non-malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. MATERIALS AND METHODS: This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. RESULTS: We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. CONCLUSION: We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian-Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.

Normotensive metabolic syndrome in Transient Receptor Potential Canonical Channel type 1 Trpc1-/- mice.

Metabolic syndrome has become a global epidemic, affecting all developed countries and communities with growing economies. Worldwide, increasing efforts have been directed at curbing this growing problem. Mice deleted of the gene encoding Type 1 Transient Receptor Potential Canonical Channel (Trpc1) were found to weigh heavier than controls. They had fasting hyperglycemia and impaired glucose tolerance compared with wild-type controls. Beyond 1 year of age, plasma triglyceride level in Trpc1-/- mice was elevated. Plasma cholesterol levels tended to be higher than in controls. The livers of Trpc1-/- mice were heavier, richer in triglyceride, and more echogenic than those of controls on ultrasound evaluation. Hematocrit was lower in Trpc1-/- mice of both genders beginning at the second to third months of age in the absence of bleeding or hemolysis. Measured by the indirect tail-cuff method or by the direct arterial cannulation, blood pressures in null mice were lower than controls. We conclude that TRPC1 gene regulates body metabolism and that except for hypertension, phenotypes of mice after deletion of the Trpc1 gene resemble mice with metabolic syndrome, suggesting that this could be a good experimental model for future investigation of the pathogenesis and management of this disorder.

Locally advanced nasopharyngeal carcinoma in adolescents treated with tomotherapy: Experience at King Faisal Specialist Hospital and Research Centre.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a rare disease worldwide; To the best of our knowledge, there is no established standard of care specifically tailored for the adolescent population. The majority of existing research relies on retrospective data analysis. OBJECTIVE: Evaluate clinical features, treatment results, prognostic factors and late toxicities of locally advanced NPC patients treated with tomotherapy. DESIGN: Retrospective. SETTINGS: Tertiary care hospital. PATIENTS AND METHODS: Between January 2007 and January 2020, we treated patients with NPC, aged between 14 and 21 years, with concomitant chemoradiotherapy using tomotherapy at our institution. We prospectively collected details of clinical characteristics, treatment modalities, outcomes and prognostic factors of these patients and then analysed them retrospectively. MAIN OUTCOME MEASURES: 3-5 years overall survival (OS), 3-5 years locoregional control rate, 3-5 years disease-free survival (DFS), prognostic factors. SAMPLE SIZE: 51 patients. RESULTS: There were 26 male and 25 female patients included in our study. The mean age was 16.5 years, 5 (9.8%) patients with stage III, and 46 (90.2%) with stage IVa according to the American Joint Committee on Cancer, 8th edition staging system. Most patients (98%) received two or more cycles of induction chemotherapy. All patients received concomitant chemoradiotherapy. The median total dose of radiotherapy delivered was 6600 cGy (range 4800-7000). With a median follow-up of 73 months (range 9-168 months), a 5-year locoregional control rate, 5-year OS and 5-year DFS rates were 100%, 86.8% and 71.7%, respectively. Five years later, disease control was 71.7%. Ten (19.6%) patients had disease recurrence in the form of distant metastases during the follow up. CONCLUSIONS: Helical tomotherapy has an excellent late toxicity profile without compromising clinical outcome for patients with NPC. Radiotherapy remains the mainstay of treatment of nasopharyngeal carcinoma to achieve remarkable local control rates. LIMITATIONS: Single institution experience, small number of patients, and retrospective design.

Altering electronic structure of nickel foam supported CoNi-based oxide through Al ions modulation for efficient oxygen evolution reaction.

Developing highly active and durable non-precious metal-based electrocatalysts for the oxygen evolution reaction (OER) is crucial in achieving efficient energy conversion. Herein, we reported a CoNiAl0.5O/NF nanofilament that exhibits higher OER activity than previously reported IrO2-based catalysts in alkaline solution. The as-synthesized CoNiAl0.5O/NF catalyst demonstrates a low overpotential of 230 mV at a current density of 100 mA cm-2, indicating its high catalytic efficiency. Furthermore, the catalyst exhibits a Tafel slope of 26 mV dec-1, suggesting favorable reaction kinetics. The CoNiAl0.5O/NF catalyst exhibits impressive stability, ensuring its potential for practical applications. Detailed characterizations reveal that the enhanced activity of CoNiAl0.5O/NF can be attributed to the electronic modulation achieved through Al3+ incorporation, which promotes the emergence of higher-valence Ni metal, facilitating nanofilament formation and improving mass transport and charge transfer processes. The synergistic effect between nanofilaments and porous nickel foam (NF) substrate significantly enhances the electrical conductivity of this catalyst material. This study highlights the significance of electronic structures for improving the activity of cost-effective and non-precious metal-based electrocatalysts for the OER.

Community-Acquired Meningitis Complicated With Pyogenic Ventriculitis and Hydrocephalus in a Patient With Haematological Malignancy: A Case Report and Literature Review.

Meningitis, an infection of the meninges of the central nervous system (CNS), can advance quickly and carries a mortality rate reaching 30% among affected patients. It may become complicated by conditions such as hydrocephalus, ventriculitis, and cerebral abscess. Here, we describe a case of meningitis that was complicated by pyogenic ventriculitis and hydrocephalus in a patient with diffuse large B-cell lymphoma (DLBCL) who underwent chemotherapy and radiotherapy. The patient presented with acute change in mental status and high-grade fever, with few episodes of non-bloody vomiting. Blood culture and cerebrospinal fluid (CSF) culture grew Streptococcus pneumoniae, which was sensitive to ceftriaxone. CT scan of the head showed ventriculomegaly, pansinusitis, and a large left mastoid effusion. MRI of the brain showed layering in ventricles, hydrocephalus, and dural enhancement consistent with pachymeningitis. She was treated with ceftriaxone for 21 days with a meaningful outcome. She was discharged home with near-baseline mental capacity for further physical therapy.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Recapitulation of Peste des Petits Ruminants (PPR) Prevalence in Small Ruminant Populations of Pakistan from 2004 to 2023: A Systematic Review and Meta-Analysis.

Peste des petits ruminants (PPR) is an extremely transmissible viral disease caused by the PPR virus that impacts domestic small ruminants, namely sheep and goats. This study aimed to employ a methodical approach to evaluate the regional occurrence of PPR in small ruminants in Pakistan and the contributing factors that influence its prevalence. A thorough search was performed in various databases to identify published research articles between January 2004 and August 2023 on PPR in small ruminants in Pakistan. Articles were chosen based on specific inclusion and exclusion criteria. A total of 25 articles were selected from 1275 studies gathered from different databases. The overall pooled prevalence in Pakistan was calculated to be 51% (95% CI: 42-60), with heterogeneity I2 = 100%, τ2 = 0.0495, and p = 0. The data were summarized based on the division into five regions: Punjab, Baluchistan, KPK, Sindh, and GB and AJK. Among these, the pooled prevalence of PPR in Sindh was 61% (95% CI: 46-75), I2 = 100%, τ2 = 0.0485, and p = 0, while in KPK, it was 44% (95% CI: 26-63), I2 = 99%, τ2 = 0.0506, and p < 0.01. However, the prevalence of PPR in Baluchistan and Punjab was almost the same. Raising awareness, proper surveillance, and application of appropriate quarantine measures interprovincially and across borders must be maintained to contain the disease.