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ABSTRACT In the pediatric age group, FXIII deficiency can present as abnormal or delayed bleeding from the umbilical stump, soft tissue and subcutaneous bleeding, intracerebral hemorrhages, intraoral bleeding, and poor wound healing. This case report describes an atypical presentation of FXIII deficiency in a 3‐year‐old female who presented to the emergency department with complaints of abdominal pain and vomiting. She was managed conservatively under the care of the pediatric surgery team.

Background Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model’s effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model’s predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.

In the rapidly advancing domain of smart manufacturing, securing data integrity and preventing unauthorized access are critical challenges. This study introduces a novel approach that synergizes anomaly detection techniques with Zero-Knowledge Proofs (ZKPs) to fortify the security framework of smart manufacturing systems. Our methodology employs a combination of data preprocessing, including statistical imputation and data smoothing, alongside advanced anomaly detection using classification methods and neural networks, particularly focusing on deep learning architectures. The detected anomalies undergo verification through zk-SNARKs, a specialized ZKP scheme, ensuring a robust validation process without compromising data confidentiality. Our findings reveal a notable enhancement in the accuracy of anomaly detection, achieving detection rates of approximately 95% for temperature fluctuations and 90% for pressure irregularities, with a significant reduction in false positives. This performance is markedly superior to traditional methods and aligns closely with the highest efficacy rates reported in contemporary studies. Moreover, the utilization of ZKPs for anomaly verification demonstrated a 98% success rate, ensuring the secure and private verification of anomalies. The integration of anomaly detection with ZKPs presents a significant leap forward in addressing the security vulnerabilities inherent in smart manufacturing. This study not only showcases the effectiveness of our approach in enhancing data security and integrity but also sets a benchmark for future research in creating more resilient and trustworthy industrial operations.

The Internet of Things (IoT) proliferation has brought unprecedented connectivity, introducing complex trust and reputation management challenges across vast, heterogeneous networks. This paper introduces the DeepTrust framework, a novel approach leveraging deep learning algorithms to dynamically assess and manage trust and reputation in IoT environments. We demonstrate the framework’s superiority in accurately identifying trustworthy and untrustworthy devices through extensive experiments, significantly enhancing IoT security. DeepTrust has demonstrated marked superiority over existing methods, showcasing enhanced accuracy in identifying trustworthy versus untrustworthy devices, thereby significantly bolstering IoT network security. Specifically, our results reveal an improvement in accuracy by 15%, precision by 20%, and recall rates by 18% compared to conventional models, highlighting DeepTrust’s effectiveness in real-time, adaptive trust assessments. There are several avenues for enhancing and expanding the DeepTrust framework. Future research will explore optimization techniques for reducing computational demands, enabling deployment on resource-constrained IoT devices. Additionally, incorporating incremental learning mechanisms could improve the framework’s adaptability to new and changing IoT environments. Enhancing data privacy and security measures within the framework constitutes another critical development area, ensuring the protection of sensitive information used in trust assessments. Lastly, extending the framework’s applicability across various IoT domains and applications presents a promising direction, aiming to establish a universal trust management solution adaptable to the unique requirements of different IoT ecosystems. By outlining these potential future directions, we aim to highlight the current achievements of the DeepTrust framework and chart a course for its continued development and refinement. This comprehensive approach underscores our commitment to advancing the field of IoT trust and reputation management, paving the way for more secure and reliable IoT networks.

Artificial Intelligence (AI) and 6G technologies promise to revolutionize the healthcare domain by enhancing the accuracy and diagnosis the patient monitoring in a real-time environment. The integration of AI and 6G technologies holds substantial promise for transforming healthcare systems. AI’s capabilities in complex data analysis, combined with the high speed and reliable 6G networks significantly improve the healthcare domain. However, the integration and application aspects of both technologies are still evolving. Thus, to fill this gap. Herein, we propose an advanced big data analytics model. Our proposed model has several phases, for instance, data collection, data selection and preprocessing, and analytical phase. Each phase has different functions applied to the preprocessed data and finally, the results are shown to the user. We have carried out several experiments and the network performance and efficiency are measured in terms of latency throughput and reliability (in terms of error rate). The achieved experimental result validate that the proposed model processed a large amount of data in a very short time. The reliability of the proposed model is better than earlier models and the execution time is efficient and also applicable in healthcare.

Pseudotumoral hemicerebellitis is a rare presentation of acute cerebellitis, which involves the inflammation of a single cerebellar hemisphere and most commonly affects children. It mimics a tumor on imaging, hence given the name. In this report, we present a case of pseudotumoral hemicerebellitis in a 30-year-old male who presented to the emergency room (ER) with complaints of vertigo, vomiting, and a headache.

Immune response elicited during pneumococcal carriage has been shown to protect against subsequent colonization and infection by Streptococcus pneumoniae. The study was designed to measure the baseline serotype-specific anti-capsular IgG concentration and opsonic titers elicited in response to asymptomatic carriage in adults with and without type 2-diabetes. Level of IgG to capsular polysaccharide was measured in a total of 176 samples (124 with type 2 diabetes and 52 without type 2 diabetes) against serotype 1, 19F, 9V, and 18C. From within 176 samples, a nested cohort of 39 samples was selected for measuring the functional capacity of antibodies by measuring opsonic titer to serotypes 19F, 9V, and 18C. Next, we measured levels of IgG to PspA in 90 samples from individuals with and without diabetes (22 non-diabetes and 68 diabetes). Our results demonstrated comparable IgG titers against all serotypes between those with and without type 2-diabetes. Overall, we observed higher opsonic titers in those without diabetes as compared to individuals with diabetes for serotypes 19F and 9V. The opsonic titers for 19F and 9V significantly negatively correlated with HbA1c. For 19F, 41.66% (n = 10) showed opsonic titers ≥ 1:8 in the diabetes group as compared to 66.66% (n = 10) in the non-diabetes group. The percentage was 29.6% (n = 7) vs 66.66% (n = 10) for 9V and 70.83% (n = 17) vs 80% (n = 12) for 18C in diabetes and non-diabetes groups respectively. A comparable anti-PspA IgG (p = 0.409) was observed in those with and without diabetes, indicating that response to protein antigen is likely to remain intact in those with diabetes. In conclusion, we demonstrated comparable IgG titers to both capsular polysaccharide and protein antigens in those with and without diabetes, however, the protective capacity of antibodies differed between the two groups.

Testicular choriocarcinomas make up less than 1% of all germ-cell tumors and are highly malignant, attributable to hematogenous spread. While the most common sites of metastasis are the lungs and liver, metastatic spread to the gastrointestinal tract is rare wherein patients may present with GI distress or even an upper GI bleed. In this report, we present a case of known testicular choriocarcinoma in a 40-year-old male who presented to the emergency room with severe anemia and a suspected upper GI bleed.

In a modern era where data-driven insights are the foundation of technological advancements, preserving the privacy and security of sensitive information while harnessing the collective intelligence of multiple parties is imperative. This research presents a Secure Collaborative Learning Algorithm (SCLA) that facilitates efficient multi-party machine learning without compromising data privacy. Our research focus is on leveraging existing, secure databases without requiring an additional data collection process. SCLA integrates homomorphic encryption and Federated Learning (FL) to enable secure data collaboration among various stakeholders. The proposed algorithm aggregates model updates in a privacy-preserving manner, demonstrating enhanced model accuracy, competitive convergence speed, and robust scalability. By carefully balancing privacy preservation and learning efficiency, the SCLA showcases a promising avenue for privacy-focused collaborative learning using existing data repositories.

Neurofibromatosis (NF) type 2 is a rare neurological, autosomal dominant and genetic disorder. It is caused by a mutation in the tumor suppressor gene, called NF2 gene. The disorder results in several benign tumors of the nervous system. These typically include vestibular schwannomas, meningiomas, and ependymomas. Multiple cranial nerve abnormalities affect the brain, spinal cord, nerves, and skin and cause significant morbidity in patients. We describe a 20-year-old patient, with a family history of brain tumors, with symptoms of left sided third nerve palsy. Magnetic Resonance Imaging (MRI) of the brain and orbits revealed a small sized cavernous sinus meningioma and bilateral vestibular schwannomas. As per the differential diagnosis and optimal resolution brain imaging, NF2 was diagnosed. The patient was referred for specific treatment to the neuro-oncology unit. The case is distinct as the patient presented with a parasellar meningioma leading to third nerve palsy besides bilateral vestibular schwannomas. Manchester criteria and high contrast MR imaging proved more beneficial in our patient for the diagnosis of a wider clinical spectrum of NF2.

Abstract Background There was a lack of information about prognostic accuracy of time to sputum culture conversion (SCC) in forecasting cure among extensively drug-resistant tuberculosis (XDR-TB) patients. Therefore, this study evaluated the prognostic accuracy of SCC at various time points in forecasting cure among XDR-TB patients. Methods This retrospective observational study included 355 eligible pulmonary XDR-TB patients treated at 27 centers in Pakistan between 01-05-2010 and 30-06-2017. The baseline and follow-up information of patients from treatment initiation until the end of treatment were retrieved from electronic nominal recording and reporting system. Time to SCC was analyzed by Kaplan–Meier method, and differences between groups were compared through log-rank test. Predictors of time to SCC and cure were respectively evaluated by multivariate Cox proportional hazards and binary logistic regression analyses. A p-value 40 years (hazards ratio [HR] = 0.632, p-value = 0.004), baseline sputum grading of scanty, + 1 (HR = 0.511, p-value = 0.002), + 2, + 3 (HR = 0.523, p-value = 0.001) and use of high dose isoniazid (HR = 0.463, p-value = 0.004) were significantly associated with early SCC. Only SCC at 6 month of treatment had statistically significant association with cure (odds ratio = 15.603, p-value

Abstract Background There was a complete lack of information about the treatment outcomes of rifampicin/multidrug resistant (RR/MDR) childhood TB patients (age ≤ 14 years) from Pakistan, an MDR-TB 5th high burden country. Therefore, this study evaluated the socio-demographic characteristics, drug resistance pattern, treatment outcomes and factors associated with unsuccessful outcomes among childhood RR/MDR-TB patients in Pakistan. Methods This was a multicentre retrospective record review of all microbiologically confirmed childhood RR/MDR-TB patients (age ≤ 14 years) enrolled for treatment at seven units of programmatic management of drug-resistant TB (PMDT) in Pakistan. The baseline and follow-up information of enrolled participants from treatment initiation until the end of treatment were retrieved from electronic nominal recording and reporting system. World Health Organization (WHO) defined criterion was used for deciding treatment outcomes. The outcomes of “cured” and “treatment completed” were collectively grouped as successful, whereas “death”, “treatment failure” and “lost to follow-up” were grouped together as unsuccessful outcomes. Multivariable binary logistic regression analysis was used to find factors associated with unsuccessful outcomes. A p-value

Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In wireless communications, the channel plays a major role in determining energy requirements. Therefore, a mixed-integer nonlinear programming problem is formulated to jointly optimize power allocation, sub-channel allocation, user selection, and the activated remote radio units (RRUs) in a combinatorial approach according to the channel conditions. Although it is an NP-hard problem, the optimization problem is solved through fractional programming properties, converting it into an equivalent tractable and parametric form. The resulting problem is then solved optimally by using the Lagrangian decomposition method and an improved Kuhn–Munkres algorithm. The results show that the proposed technique significantly improves the energy efficiency of IoT systems as compared to the state-of-the-art work.

Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

Herein, a molecularly imprinted polymer (MIP) was prepared using bulk polymerization and applied to wastewater to aid the adsorption of targeted template molecules using ethylene glycol dimethacrylate (EGDMA), methacrylic acid (MAA), acid black-234 (AB-234), 2,2′-azobisisobutyronitrile (AIBN), and methanol as a cross linker, functional monomer, template, initiator, and porogenic solvent, respectively. For a non-molecularly imprinted polymer (NIP), the same procedure was followed but without adding a template. Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and a surface area analyzer were used to determine the surface functional groups, morphology and specific surface area of the MIP and NIP. At pH 5, the AB-234 adsorption capability of the MIP was higher (94%) than the NIP (31%). The adsorption isotherm data of the MIP correlated very well with the Langmuir adsorption model with Qm 82, 83 and 100 mg/g at 283 K, 298 K, and 313 K, respectively. The adsorption process followed pseudo–second-order kinetics. The imprinted factor (IF) and Kd value of the MIP were 5.13 and 0.53, respectively. Thermodynamic studies show that AB-234 dye adsorption on the MIP and NIP was spontaneous and endothermic. The MIP proved to be the best selective adsorbent for AB-234, even in the presence of dyes with similar and different structures than the NIP.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

Background: Evaluating treatment outcomes of a cohort of patients is an effective way for analyzing the effectiveness of a program. Information regarding drug resistance pattern, detailed management, treatment outcomes and factors associated with unsuccessful outcomes in multidrug resistant (MDR-TB) patients is missing from Baluchistan province of Pakistan. Methods: This study was carried out at Programmatic Management of Drug Resistant TB unit at Fatimah Jinnah General and Chest Hospital Quetta. All eligible 186 MDR-TB patients enrolled at the study site from January 1, 2012 to April 30, 2016 were retrospectively followed until the treatment outcomes were reported. Data was abstracted through a standardized data collection form and analysed by SPSS 20. Multivariate binary logistic regression (MVBLR) analysis was used to evaluate factors associated with i) death and treatment failure and ii) lost to follow up. A p-value of 40 years (OR = 4.249, p-value = 0.001) had statistically significant positive and baseline body weight of >40 kg (OR = 0.256, p-value = 0.002) had statistically significant negative association with death and treatment failure. No factor had statistical significant association with lost to follow up. Conclusion: Overall treatment success rate was promising but did not achieve the target success rate (>75%) set by World Health Organization. It can be further improved by paying special attention and providing enhanced management to the patients with risk factors for unsuccessful outcomes. Keywords: Baluchistan, Body weight, Death, MDR-TB, Ofloxacin

Background: The current study is conducted with the aim to the fill the gap of information regarding treatment outcomes and variables associated with unsuccessful outcome among XDR-TB patients from Pakistan.Methods: A total of 404 culture confirmed XDR-TB patients who received treatment between 1st May 2010 and June 30, 2017 at 27 treatment centers all over Pakistan were retrospectively followed until their treatment outcomes were reported. A p-value 60 years (OR = 4.69, 95%CI:1.57–15.57) and receiving high dose isoniazid (OR = 2.36, 95%CI:1.14–4.85) had statistically significant positive association with death, whereas baseline body weight >40 kg (OR = 0.43, 95%CI:0.25–0.73) and sputum culture conversion in the initial two months of treatment (OR = 0.33, 95%CI:0.19–0.58) had statistically significant negative association with death. Moreover, male gender had statistically significant positive association (OR = 1.92, 95%CI:1.04–3.54) with LTFU.Conclusion: The treatment success rate (40.6%) of XDR-TB patients in Pakistan was poor. Providing special attention and enhanced clinical management to patients with identified risk factors for death and LTFU in the current cohort may improve the treatment outcomes.

A molecularly imprinting polymer (MIP) was synthesized for Basic Blue 3 dye and applied to wastewater for the adsorption of a target template. The MIPs were synthesized by bulk polymerization using methacrylic acid (MAA) and ethylene glycol dimethacrylate (EGDMA). Basic Blue 3 dye (BB-3), 2,2′-azobisisobutyronitrile (AIBN) and methanol were used as a functional monomer, cross linker, template, initiator and porogenic solvent, respectively, while non-imprinting polymers (NIP) were synthesized by the same procedure but without template molecules. The contact time was 25 min for the adsorption of BB-3 dye from 10 mL of spiked solution using 25 mg polymer. The adsorption of dye (BB-3) on the MIP followed the pseudo-second order kinetic (k2 = 0.0079 mg·g−1·min−1), and it was according to the Langmuir isotherm, with maximum adsorption capacities of 78.13, 85.4 and 99.0 mg·g−1 of the MIP at 283 K, 298 K and 313 K, respectively and 7 mg·g−1 for the NIP. The negative values of ΔG° indicate that the removal of dye by the molecularly imprinting polymer and non-imprinting polymer is spontaneous, and the positive values of ΔH° and ΔS° indicate that the process is endothermic and occurred with the increase of randomness. The selectivity of the MIP for BB-3 dye was investigated in the presence of structurally similar as well as different dyes, but the MIP showed higher selectivity than the NIP. The imprinted polymer showed 96% rebinding capacity at 313 K towards the template, and the calculated imprinted factor and Kd value were 10.73 and 2.62, respectively. In this work, the MIP showed a greater potential of selectivity for the template from wastewater relative to the closely similar compounds.

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Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

Microvirin (MVN) is one of the human immunodeficiency virus (HIV-1) entry inhibitor lectins, which consists of two structural domains sharing 35% sequence identity and contrary to many other antiviral lectins, it exists as a monomer. In this study, we engineered an MVN variant, LUMS1, consisting of two domains with 100% sequence identity, thereby reducing the chemical heterogeneity, which is a major factor in eliciting immunogenicity. We determined carbohydrate binding of LUMS1 through NMR chemical shift perturbation and tested its anti-HIV activity in single-round infectivity assay and its anti-hepatitis C virus (HCV) activity in three different assays including HCVcc, HCVpp, and replicon assays. We further investigated the effect of LUMS1 on the activation of T helper (Th) and B cells through flow cytometry. LUMS1 showed binding to α(1-2)mannobiose, the minimum glycan epitope of MVN, potently inhibited HIV-1 and HCV with EC50 of 37.2 and 45.3 nM, respectively, and showed negligible cytotoxicity with CC50 > 10 µM against PBMCs, Huh-7.5 and HepG2 cells, and 4.9 µM against TZM-bl cells. LUMS1 did not activate Th cells, and its stimulatory effect on B cells was markedly less as compared to MVN. Together, with these effects, LUMS1 represents a potential candidate for the development of antiviral therapies.

Background: Fluoroquinolones are the backbone of multidrug resistant tuberculosis treatment regimens. Despite the high burden of multidrug resistant tuberculosis in the country, little is known about drug resistance patterns, prevalence, and predictors of fluoroquinolones resistance among multidrug resistant tuberculosis patients from Pakistan. Objective: To evaluate drug resistance patterns, prevalence, and predictors of fluoroquinolones resistance in multidrug resistant tuberculosis patients. Methods: This was a cross-sectional study conducted at a programmatic management unit of drug resistant tuberculosis, Lady Reading Hospital Peshawar, Pakistan. Two hundred and forty-three newly diagnosed multidrug resistant tuberculosis patients consecutively enrolled for treatment at study site from January 1, 2012 to July 28, 2013 were included in the study. A standardized data collection form was used to collect patients’ socio-demographic, microbiological, and clinical data. SPSS 16 was used for data analysis. Results: High degree of drug resistance (median 5 drugs, range 2–8) was observed. High proportion of patients was resistant to all five first-line anti-tuberculosis drugs (62.6%), and more than half were resistant to second line drugs (55.1%). The majority of the patients were ofloxacin resistant (52.7%). Upon multivariate analysis previous tuberculosis treatment at private (OR = 1.953, p = 0.034) and public private mix (OR = 2.824, p = 0.046) sectors were predictors of ofloxacin resistance. Conclusion: The high degree of drug resistance observed, particularly to fluoroquinolones, is alarming. We recommend the adoption of more restrictive policies to control non-prescription sale of fluoroquinolones, its rational use by physicians, and training doctors in both private and public–private mix sectors to prevent further increase in fluoroquinolones resistant Mycobacterium tuberculosis strains. Keywords: Fluoroquinolones, MDR-TB, Resistance, Private

BACKGROUND: Various studies have reported culture conversion at two months as a predictor of successful treatment outcome in multidrug-resistant tuberculosis (MDR-TB). OBJECTIVES: The present study was conducted with the aim to evaluate the rate and predictors of culture conversion at two months in MDR-TB patients. METHODS: All confirmed pulmonary MDR-TB patients enrolled for treatment at Lady Reading Hospital Peshawar, Pakistan from 1 January to 31 December 2012 and met the inclusion criteria were reviewed retrospectively. Rate and predictors of culture conversion at two months were evaluated. RESULTS: Eighty seven (53.4%) out of 163 patients achieved culture conversion at two months. In a multivariate analysis lung cavitation at baseline chest X-ray (P = 0.006, OR = 0.349), resistance to ofloxacin (P = 0.041, OR = 0.193) and streptomycin (P = 0.017, OR = 0.295) had statistically significant (P

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease. [ABSTRACT FROM AUTHOR]

A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule. Nevertheless, accurately finding splice sequence sites using various molecular biology techniques and other biological approaches is complex and time-consuming. This paper presents a precise and reliable computer-aided diagnosis (CAD) technique for the rapid and correct identification of splice site sequences. The proposed deep learning-based framework uses long short-term memory (LSTM) to extract distinct patterns from RNA sequences, enabling rapid and accurate point mutation sequence mapping. The proposed network employs one-hot encodings to find sequential patterns that effectively identify splicing sites. A thorough ablation study of traditional machine learning, one-dimensional convolutional neural networks (1D-CNNs), and recurrent neural networks (RNNs) models was conducted. The proposed LSTM network outperformed existing state-of-the-art approaches, improving accuracy by 3% and 2% for the acceptor and donor sites datasets. [ABSTRACT FROM AUTHOR]