Antiseizure Activity of Mitragyna inermis in the Pentylenetetrazol-Induced Seizure Model in Mice: Involvement of Flavonoids and Alkaloids.

Objective: This study aimed to investigate whether Mitragyna inermis (Willd.) Otto Kuntze organic and aqueous extracts are able to control seizures induced by pentylenetetrazol (PTZ) in mice based on flavonoid fingerprints and alkaloidal contents. Materials and Methods: Ethanolic extract and decoction-derived fractions from roots, leaves, and stems were subjected to chromatographic fingerprinting using AlCl3 and screening for their antiseizure effects using PTZ-induced acute seizure model. From the fractions that showed potent bioactivities, plausible antiseizure alkaloids were isolated using thin layer chromatography, and their structures were elucidated using 1H NMR, 2D NMR, 13C NMR, and FAB-HR (+ve or -ve). Results: All fractions, with the exception of the dichloromethane and hexane fractions, revealed remarkable flavonoid fingerprints. An acute PTZ-induced seizure test revealed that ethanolic extract of stem bark [500 mg/kg body weight (bw)], ethyl acetate extract of stem bark (500 mg/kg bw), and aqueous extract of leaves (300 mg/kg bw) significantly delayed the occurrence of hind limb tonic extension (HLTE); however, a non-significant delay was observed in the onset of first myoclonic jerk compared with control animals. Isolation yielded four main alkaloids: that are, pteropodine (1), isopteropodine (2), mitraphylline (3) and corynoxeine (4). Corynoxeine is a new compound derived from M. inermis. Conclusion: This study suggests that flavonoid fingerprints are tracers of M. inermis anticonvulsant ingredients. The stem bark ethanolic and ethyl acetate extracts and leaf aqueous extracts contain anticonvulsant bioactive principles that delay notifying the HLTE occurring in male naval medical research institute mice. Furthermore, alkaloidal contents also remain plausible bioactive anticonvulsant principles. All observations support the traditional use of M. inermis to manage epilepsy. However, further studies are needed to understand the effects of alkaloid fractions, flavonoids, and the isolated compounds as promising antiseizure agents derived from M. inermis in experimental animals.

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson's disease behavioral analysis.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

A CMIP6 multi-model based analysis of potential climate change effects on watershed runoff using SWAT model: A case study of kunhar river basin, Pakistan.

The hydrological regimes of watersheds might be drastically altered by climate change, a majority of Pakistan's watersheds are experiencing problems with water quality and quantity as a result precipitation changes and temperature, necessitating evaluation and alterations to management strategies. In this study, the regional water security in northern Pakistan is examined about anthropogenic climate change on runoff in the Kunhar River Basin (KRB), a typical river in northern Pakistan using Soil and Water Assessment tool (SWAT) and flow durarion curve (FDC). Nine general circulation models (GCMs) were successfully utilized following bias correction under two latest IPCC shared socioeconomic pathways (SSPs) emission scenarios. Correlation coefficients (R2), Nash-Sutcliffe efficiency coefficients (NSE), and the Percent Bias (PBIAS) are all above 0.75. The conclusions demonstrate that the SWAT model precisely simulates the runoff process in the KRB on monthly and daily timescales. For the two emission scenarios of SSP2-4.5 and SSP5-8.5, the mean annual precipitation is predicted to rise by 3.08 % and 5.86 %, respectively, compared to the 1980-2015 baseline. The forecasted rise in mean daily high temperatures is expected to range from 2.08 °C to 3.07 °C, while the anticipated increase in mean daily low temperatures is projected to fall within the range of 2.09 °C-3.39 °C, spanning the years 2020-2099. Under the two SSPs scenarios, annual runoff is estimated to increase by 5.47 % and 7.60 % due to climate change during the same period. Future socioeconomic growth will be supported by a sufficient water supply made possible by the rise in runoff. However, because of climate change, there is a greater possibility of flooding because of increases in both rainfall and runoff. As a result, flood control and development plans for KRB must consider the climate change's possible effects. There is a chance that the peak flow will move backwards relative to the baseline.

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

Matrin-3 acts as a potential biomarker and promotes hepatocellular carcinoma progression by interacting with cell cycle-regulating genes.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. The oncogenic role of Matrin-3 (MATR3), an a nuclear matrix protein, in HCC remains largely unknown. Here, we document the biological function of MATR3 in HCC based on integrated bioinformatics analysis and functional studies. According to the TCGA database, MATR3 expression was found to be positively correlated with clinicopathological characteristics in HCC. The receiver operating characteristic (ROC) curve and Kaplan-Meier (KM) curve displayed the diagnostic and prognostic potentials of MATR3 in HCC patients, respectively. Pathway enrichment analysis represented the enrichment of MATR3 in various molecular pathways, including the regulation of the cell cycle. Functional assays in HCC cell lines showed reduced proliferation of cells with stable silencing of MATR3. At the same time, the suppressive effects of MATR3 depletion on HCC development were verified by xenograft tumor experiments. Moreover, MATR3 repression also resulted in cell cycle arrest by modulating the expression of cell cycle-associated genes. In addition, the interaction of MATR3 with cell cycle-regulating factors in HCC cells was further corroborated with co-immunoprecipitation and mass spectrometry (Co-IP/MS). Furthermore, CIBERSORT and TIMER analyses showed an association between MATR3 and immune infiltration in HCC. In general, this study highlights the novel oncogenic function of MATR3 in HCC, which could comprehensively address how aberrant changes in the cell cycle promote HCC development. MATR3 might serve as a prognostic predictor and therapeutic target for HCC patients.

A prospective observational study of estimating drug related problems and clinical outcomes in subtypes of stroke patients.

BACKGROUND: Stroke is a neurological disease and a leading cause of mortality worldwide. Strokes mainly consist of two types: hemorrhage and ischemia. Stroke patients are being administered multiple drug therapy and are at risk of drug-related problems. AIM: To estimate drug-related problems (DRPs) and clinical end outcomes in hospitalized stroke patients. METHODS: Current study was a multicenter, cross-sectional prospective observational study including 250 stroke patients admitted to tertiary care hospitals in Karachi, Pakistan. The study included all clinical subtypes of stroke patients i.e. Stroke, Ischemic stroke, Hemorrhagic stroke, CVA, and TIA. Associations among patient-clinical end outcomes and drug therapy-related variables like DRPs, mortality, and morbidity rates were estimated using Pearson's chi-squared test. Statistical analysis was done by using SPSS software, version 25. RESULTS: A total of 250 patients participated in this study suffering from different clinical subtypes of stroke i.e. Ischemic stroke, hemorrhagic stroke, TIA, and CVA, including 46% male and 54% female patients. The majority of patients' stay at the hospital was between 1-10 days. The overall mortality rate in stroke patients was 51%. HAIs were observed in 70% of patients, HAIs faced by patients were SAP, CAP, UTI, sepsis, and VAP. Drugs were assessed according to NEML i.e. access group antibiotics, watch group antibiotics, reserve group antibiotics, statins, antiepileptics, and proton pump inhibitors. Majorly ceftriaxone was administered to 79% of patients, piperacillin-tazobactam to 52%, and cefixime to 48%, whereas meropenem was administered to 42% of patients along with vancomycin to 39% of total patients. A high mortality rate was observed in the case of Klebsiella pneumoniae and Staphylococcus aureus i.e. 78% and in the case of streptococcus pneumoniae 61% mortality rate was observed. Due to the presence of DRPs and various other clinical factors like comorbidities, DDIs, HAIs, administration of potentially nephrotoxic drugs, and administration of antibiotics without having CST, hospitalized stroke patients faced many problems. CONCLUSION: This study helped determine DRPs along with various clinical factors affecting the clinical end outcomes of patients suffering from any clinical subtype of stroke. Due to the enhancement in the evidence of the incidence of DRPs in tertiary care hospitals, pharmacist-led drug therapy review by interfering with doctors and other medical professionals at the patient bed site is needed and should be done to avoid any negative end outcomes and serious issues related to DRPs.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish.

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

Propagation and reflection of thermoelastic wave in a rotating nonlocal fractional order porous medium under Hall current influence.

This investigation relates to the research on Hall current on propagation and reflection of elastic waves through non-local fractional-order thermoelastic rotating medium with voids. The system is split up into longitudinal and transverse components using the Helmholtz vector rule. It is observed that, through the frequency dispersion relation four coupled quasi-waves exist in the medium. The rotating solid modifies the nature of purely longitudinal and transverse waves toward the quasi-type waves. All the propagating waves are dispersive as they depend upon angular frequency. The quasi-longitudinal wave qP and quasi-transverse wave qSV faces cut-off frequencies. The nonlocal parameter affect all the waves except the quasi void wave. Analytically, the reflection coefficients of the wave are computed using suitable boundary conditions. MATLAB software is used to perform numerical computations for a chosen solid material. The amplitude ratios and the speed of propagation of the wave are plotted graphically for rotational frequency, nonlocal, fractional order, and Hall current parameter. The significant effect of the physical parameters on the computed results has been observed. The cut-off frequency of the waves is also presented graphically. The energy conservation law is proved in the form of energy ratios. The earlier findings in the literature are obtained as special case in the absence of rotation, Hall current parameter and porous voids.

The essential impact of stress appraisals on work engagement.

This paper explains the contradictory findings on the relationship between stress and work engagement by including appraisals as a driving mechanism through which job stressors influence engagement. In doing so, it explores whether stressors categorised as either challenging or hindering can be appraised simultaneously as both. Second, it investigates whether stress mindset explains not only how stressors are appraised, but also how appraisals influence engagement. Over five workdays, 487 Canadian and American full-time employees indicated their stress mindset and appraised numerous challenging and hindering stressors, after which they self-reported their engagement at work. Results showed that employees rarely appraised stress as uniquely challenging or hindering. Moreover, when employees harbored positive views about stress, stressors overall were evaluated as less hindering and hindrance stressors were particularly more challenging. Stress mindset appears to be critical in modulating the genesis of stress appraisals. In turn, appraisals explained the stressor-engagement relationship, with challenge and hindrance stressors boosting and hampering engagement, respectively. Finally, positive stress mindset buffered the negative effect of hindrance appraisals on engagement. Our findings clarify misconceptions about how workplace stressors impact engagement and offer novel evidence that stress mindset is a key factor in stress at work.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

Objective: This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. Method: The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture. Results: The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. Conclusion: Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance.

CCR9 overexpression promotes T-ALL progression by enhancing cholesterol biosynthesis.

Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of the lymphoid progenitor cells, contributing to ∼ 20% of the total ALL cases, with a higher prevalence in adults than children. Despite the important role of human T-ALL cell lines in understanding the pathobiology of the disease, a detailed comparison of the tumorigenic potentials of two commonly used T-ALL cell lines, MOLT4 and JURKAT cells, is still lacking. Methodology: In the present study, NOD-Prkdc scid IL2rgd ull (NTG) mice were intravenously injected with MOLT4, JURKAT cells, and PBS as a control. The leukemiac cell homing/infiltration into the bone marrow, blood, liver and spleen was investigated for bioluminescence imaging, flow cytometry, and immunohistochemistry staining. Gene expression profiling of the two cell lines was performed via RNA-seq to identify the differentially expressed genes (DEGs). CCR9 identified as a DEG, was further screened for its role in invasion and metastasis in both cell lines in vitro. Moreover, a JURKAT cell line with overexpressed CCR9 (Jurkat-OeCCR9) was investigated for T-ALL formation in the NTG mice as compared to the GFP control. Jurkat-OeCCR9 cells were then subjected to transcriptome analysis to identify the genes and pathways associated with the upregulation of CCR9 leading to enhanced tumirogenesis. The DEGs of the CCR9-associated upregulation were validated both at mRNA and protein levels. Simvastatin was used to assess the effect of cholesterol biosynthesis inhibition on the aggressiveness of T-ALL cells. Results: Comparison of the leukemogenic potentials of the two T-ALL cell lines showed the relatively higher leukemogenic potential of MOLT4 cells, characterized by their enhanced tissue infiltration in NOD-PrkdcscidIL2rgdull (NTG) mice. Transcriptmoe analysis of the two cell lines revealed numerous DEGs, including CCR9, enriched in vital signaling pathways associated with growth and proliferation. Notably, the upregulation of CCR9 also promoted the tissue infiltration of JURKAT cells in vitro and in NTG mice. Transcriptome analysis revealed that CCR9 overexpression facilitated cholesterol production by upregulating the expression of the transcriptional factor SREBF2, and the downstream genes: MSMO1, MVD, HMGCS1, and HMGCR, which was then corroborated at the protein levels. Notably, simvastatin treatment reduced the migration of the CCR9-overexpressing JURKAT cells, suggesting the importance of cholesterol in T-ALL progression. Conclusions: This study highlights the distinct tumorigenic potentials of two T-ALL cell lines and reveals CCR9-regulated enhanced cholesterol biosynthesis in T-ALL.

Multi-step attack detection in industrial networks using a hybrid deep learning architecture.

In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information. To ensure the effectiveness of detection in industrial networks, which necessitate continuous operation and undergo changes over time, intrusion detection algorithms should possess the capability to automatically adapt to these changes. Several researchers have focused on the automatic detection of these attacks, in which deep learning (DL) and machine learning algorithms play a prominent role. This study proposes a hybrid model that combines two DL algorithms, namely convolutional neural networks (CNN) and deep belief networks (DBN), for intrusion detection in industrial networks. To evaluate the effectiveness of the proposed model, we utilized the Multi-Step Cyber Attack (MSCAD) dataset and employed various evaluation metrics.

An efficient controller design of a PV integrated single inductor multiple output DC-DC converter for dynamic voltage and low power applications.

In this article, a Single Inductor Multiple Output (SIMO) DC-DC boost converter for driving independent three outputs of dynamic voltage and low power is proposed. Compared with the traditional SIMO DC-DC converter, the proposed work accomplishes (i) independent control of power at each output, (ii) small switch count, (iii) relatively better scalability with increasing output channel, (iv) fast response time, and (v) relatively better efficiency. The core aim of the current article is to implement an efficient controller design of the SIMO DC converter circuit operating in continuous conduction mode for dynamic voltage applications. The SIMO circuit comprises a photovoltaic (PV) array as a renewable energy harvesting source at the input. A hysteretic controller based on direct control with seamless transition control topology is implemented in this model for SIMO operation. This scheme is popular due to its less cost, simple, easy-to-use design architecture, and fast speed of close loop control. The configuration of the solar array is designed to deliver maximum power on the input of the SIMO DC-DC converter. For tracking the maximum power point, incremental conductance with integral control configuration is applied on the PV array with fast speed and efficiency. The MATLAB/Simulink environment is utilized for model configuration. The simulation results show that the proposed SIMO configuration with the PV array provided 100.5 W and 21.7 W for 1000 W/m2 and 250 W/m2, respectively.

Exploring multi-level risk factors and post-war burdens of trachomatous trichiasis among displaced population in Raya Kobo districts, implication for urgent action.

AIM: To estimate post-war burdens of trachomatous trichiasis (TT) and multi-level risk factors among displaced population in Raya Kobo districts, implication for urgent action. METHODS: A community-based cross-sectional study was conducted among 603 participants from randomly selected 14 displaced slums in the Raya Kobo district. The data was collected from February 16th to March 30th, 2023. Study participants were selected using the multistage sampling technique. A structured questionnaire and ophthalmic loupe with ×2.5 magnificence were used to collect from participants. Multi-level binary logistic regression was used to determine associated factors with TT infection. Adjusted odds ratio (AOR) with 95% confidence interval (CI) were claimed for the strength of association at P<0.05. RESULTS: We recruited 602 (99.9%) participants for the final analysis. From the total, 126 (20.9%) and 98 (16.3%, 95%CI: 13.5%-19.4%) participants were diagnosed with active trachoma & TT infection, respectively. Being age ≥45y (AOR=7.9, 95%CI: 2.4-25.3), having multiple eye infections (AOR=2.73, 95%CI: 1.47-5.29), poor wealth index (AOR=9.2; 95%CI: 2.7-23.7) and twice face washing per day (AOR=0.082, 95%CI: 0.03-0.21) has identified as individual as factors for TT infection. Whereas, distance between clean water source ≥10 km (AOR=6.5, 95%CI: 3.9-31.3), and latrine availability (AOR=0.35, 95%CI: 0.21-0.58) were the two community-level factors associated with TT infections. CONCLUSION: The high prevalence of TT infection post-war throughout the study districts indicates a need for urgent clinical intervention in addition to rapid scaling up surgery, antibiotics, facial cleanliness, and environmental improvement (SAFE) strategies, strategy for high-risk population. Age≥45y, distance from the clean water source, poor wealth indexes, and eye infection are identified to be risk factors for TT infection. Furthermore, community-level preventative factors for TT infection are found as latrine availability and face washing practice.

Prevalence of active trachoma infection and associated factors post-war resettled population in raya kobo districts, North East Ethiopia: A community-based cross-sectional study in 2022.

Background: Active trachoma infection poses a serious threat to public health, particularly for those who live in an unprivileged area and has practiced open-field defecation. This study aimed to estimate the prevalence of active trachoma infection and associated factors in the post-war resettled population in Raya Kobo district, North East Ethiopia: a community-based cross-sectional study in 2022. Methods: A community-based cross-sectional study was conducted among 602 participants randomly selected in 14 slum villages in Raya Kobo from February 16th to March 30th, 2023. After the data was collected using a semi-structured questionnaire and entered into Epi-data version 3.2. The study participants were chosen using a two-stage sampling process. Binary logistic regression was used to identify factors for active trachoma infection. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were claimed for the strength of association at p < 0.05. Results: Overall, 602 (99.9%) study participants were included in the final analysis. At the end of the study period, 126 (20.9) participants developed active trachoma infection. On multivariable analysis, were aged ≥45 years (AOR = 7.9, 95% CI = 2.4-25.3), history of eye infection (AOR = 3.7, 95% CI = 2.4-10.4, p = 0.001), were poor wealth index (AOR = 9.2, 95% CI = 2.7-23.7), having separated kitchen (AOR = 4.05, 95% CI = 1.86-8.86), living with animals (AOR = 5.92, 95% CI = 2.31-14.7) and having got administration of mass-drug (AOR = 8.9, 95% CI = 2.36-33.6) were significant risk factors for active trachoma infection. Whereas, face washing practice regularly (AOR = 0.23, 95% CI = 0.127-0.43), and toilet availability (AOR = 0.35, 95% CI = 0.20-0.97) were preventive factors for active trachoma infection. Conclusion: A significant prevalence of active trachoma infection was reported in the area as compared with previous findings and urgent clinical intervention, and the WHO critical SAFE strategies (surgery, antibiotics, facial cleanliness, and environmental improvement) implementation is highly needed in the area. In addition, healthcare providers should focus on information dissemination on proper latrine utilization, and washing the face regularly to prevent active trachoma infection is highly recommended.

Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.

In the Internet of things (IoT), data packets are accumulated and disseminated across IoT devices without human intervention, therefore the privacy and security of sensitive data during transmission are crucial. For this purpose, multiple routing techniques exist to ensure security and privacy in IoT Systems. One such technique is the routing protocol for low power and lossy networks (RPL) which is an IPv6 protocol commonly used for routing in IoT systems. Formal modeling of an IoT system can validate the reliability, accuracy, and consistency of the system. This paper presents the formal modeling of RPL protocol and the analysis of its security schemes using colored Petri nets that applies formal validation and verification for both the secure and non-secure modes of RPL protocol. The proposed approach can also be useful for formal modeling-based verification of the security of the other communication protocols.

Factors affecting superovulation induction in goats (Capra hericus): An analysis of various approaches.

Goats are generally called a "poor man's cow" because they not only provide meat and milk but also other assistance to their owners, including skins for leather production and their waste, which can be used as compost for fertilizer. Multiple ovulation and embryo transfer (MOET) is an important process in embryo biotechnology, as it increases the contribution of superior female goats to breeding operations. The field of assisted reproductive biotechnologies has seen notable progress. However, unlike in cattle, the standard use of superovulation and other reproductive biotechnologies has not been widely implemented for goats. Multiple intrinsic and extrinsic factors can alter the superovulatory response, significantly restricting the practicability of MOET technology. The use of techniques to induce superovulation is a crucial step in embryo transfer (ET), as it accelerates the propagation of animals with superior genetics for desirable traits. Furthermore, the conventional superovulation techniques based on numerous injections are not appropriate for animals and are labor-intensive as well as expensive. Different approaches and alternatives have been applied to obtain the maximum ovarian response, including immunization against inhibin and the day-0 protocol for the synchronization of the first follicular wave. While there are several studies available in the literature on superovulation in cattle, research on simplified superovulation in goats is limited; only a few studies have been conducted on this topic. This review describes the various treatments with gonadotropin that are used for inducing superovulation in various dairy goat breeds worldwide. The outcomes of these treatments, in terms of ovulation rate and recovery of transferrable embryos, are also discussed. Furthermore, this review also covers the recovery of oocytes through repeated superovulation from the same female goat that is used for somatic cell nuclear transfer (SCNT).

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery.

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

Clinical Outcomes Following the Implementation of a Novel One-Year Training Program in Emergency Medicine in Karachi, Pakistan.

Background: Most Emergency Departments (EDs) in low- and middle-income countries (LMICs), particularly in Pakistan, are staffed by physicians not formally trained in Emergency Medicine (EM). As of January 2022, there were only 13 residency training programs in EM throughout all of Pakistan. Therefore, an intermediate solution-a one-year training program in EM-was developed to build capacity. Objective: To determine the impact of a novel training program in EM on clinical metrics and outcomes. Methods: The first cohort of a novel, one-year training program-the Certification Program in Emergency Medicine (CPEM)-completed the program in June 2019. The program consisted of two arms: CPEM-Clinical (CPEM-C), which included physicians from the Indus Hospital and Health Network (IHHN) ED; and CPEM-Didactic (CPEM-D), which included physicians from EDs across Karachi. Both groups participated in weekly conferences, such as didactics, small group discussions, workshops, and journal clubs. CPEM-C learners also received clinical mentorship from local and international faculty. Mortality, length of stay (LOS), and time-to-evaluation, as well as metrics in four key areas-patients at risk for cardiovascular disease/acute coronary syndrome, sepsis, respiratory illness, and intra-abdominal trauma-were assessed before and after the initial cohort at IHHN and compared with other groups in IHHN. Findings and Conclusions: More than 125,000 patients were seen from July to December 2017 (pre-CPEM) and July to December 2019 (post-CPEM). Overall, there were significant improvements in all clinical metrics and outcomes, with the exception of LOS and time-to-evaluation, and a trend toward improved mortality. In comparing CPEM graduates to other groups in IHHN ED, most metrics and outcomes significantly improved or trended toward improvement, including mortality. Implementation of a medium-duration, intensive EM training program can help improve patient care and the development of EM as a new specialty in lower-resource settings.

LncRNA15691 promotes T-ALL infiltration by upregulating CCR9 via increased MATR3 stability.

Our previous studies demonstrated that CCR9 plays an important role in several aspects of T-cell acute lymphoblastic leukemia progression and that CCR9 is a potential therapeutic target. However, the underlying mechanism that regulates CCR9 expression remains incompletely understood. In this study, bioinformatics analysis and validation in clinical samples revealed the lncRNA15691 to be positively correlated with CCR9 mRNA expression and significantly upregulated in T-cell acute lymphoblastic leukemia samples and CCR9high T-cell acute lymphoblastic leukemia cell lines. LncRNA15691, a previously uncharacterized lncRNA, was found to be located in both the cytoplasm and the nucleus via fluorescence in situ hybridization assay. In addition, lncRNA15691 upregulated the expression of CCR9 and was involved in T-cell acute lymphoblastic leukemia cell invasion. In vivo experiments showed that lncRNA15691 promoted leukemia cell homing/infiltration into the bone marrow, blood, and spleen, whereas the CCR9 ligand, CCL25, augmented the extramedullary infiltration of CCR9low leukemia cells overexpressing lncRNA15691 into blood, spleen, and liver. Subsequently, RNA protein pull-down assays, coupled with liquid chromatography-tandem mass spectrometry, were used to uncover potential lncRNA15691-interacting proteins, which were then validated by RNA immunoprecipitation. These mechanistic studies revealed that lncRNA15691 upregulated CCR9 expression via directly binding to and stabilizing MATR3 by inhibiting its nuclear degradation mediated by PKA. Collectively, our study revealed a novel mechanism of regulating CCR9 expression and implicated lncRNA15691 as a potential novel biomarker for T-cell acute lymphoblastic leukemia infiltration.