Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework.

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.

Role of Anteromedial Cortical Support for Unstable Intertrochanteric Fractures Being Treated With Cephalomedullary Nails.

INTRODUCTION: Reduction quality is of paramount importance for an optimal outcome in unstable pertrochanteric fractures. The non-anatomical functional anteromedial buttress is proposed to prevent impending mechanical complications. We aimed to evaluate the role of anteromedial cortical support in preventing mechanical complications following fixation with the cephalomedullary nail. MATERIALS AND METHODS: A prospective, single-arm interventional study was conducted in the Orthopaedics Department of a Combined Military Hospital (CMH) in Rawalpindi. The duration of the study was 24 months. Patients were recruited by the purposive sampling technique as per inclusion/exclusion criteria. Preoperatively, the reduction was categorized as per Baumgartner's and Chang's criteria. Post-operatively, weight bearing as tolerated was advised. Radiographs prior to discharge for loss of reduction were evaluated. Follow-up radiographic measurements of neck length, neck shaft angle, and their loss as per protocol were done at three and six months. RESULTS: A total of 202 patients were operated on from October 21 until August 23. Mortality at six months in 39 patients (19.3%) and loss to follow-up in 31 patients (15.3%) resulted in 132 patients with complete follow-up and having developed complications in 12 patients (9.09%). The mean age was 76.3 ± 7.98 years; males were 105 (79.5%), and females were 27 (20.5%). Closed reduction was 58 (43.9%), and additional manoeuvres were required in 74 (56.1%). The mean tip apex distance (TAD) was 24.56 ± 2.76, and the Calcar gap was 5.16 ± 1.27. Cleveland zone centre-centre in 54 (40.9%), inferior-centre in 65 (49.2%), and inferior-posterior (9.9%) were statistically significant for mechanical complications (p≤0.001). There was a significant association between the grading of Chang's and Baumgartner's poor groups for the development of mechanical complications (p≤0.001). The mean time to full weight bearing without support was 21 ± 1.22 weeks. The mean Hip Harris score at six months was 69.27 ± 7.68. CONCLUSION: Results suggest that anteromedial cortical support can lead to fewer potential mechanical complications at six months. A higher Chang's grade drives surgeons to engage in additional manoeuvres. Anteromedial cortical support is worth consideration for unstable pertrochanteric fractures.

Immediate Versus Staged Complete Revascularization in Patients With Acute Coronary Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

A strategy of complete revascularization (CR) is recommended in patients with acute coronary syndrome (ACS) and multivessel disease (MVD). However, the optimal timing of CR remains equivocal. We searched MEDLINE, Embase, the Cochrane Library, and ClinicalTrials.gov for randomized controlled trials (RCTs) comparing immediate CR (ICR) with staged CR in patients with ACS and MVD. Our primary outcomes were all-cause and cardiovascular mortality. All outcomes were assessed at 3 time points: in-hospital or at 30 days, at 6 months to 1 year, and at >1 year. Data were pooled in RevMan 5.4 using risk ratios as the effect measure. A total of 9 RCTs (7,506 patients) were included in our review. A total of 7 trials enrolled patients with ST-segment elevation myocardial infarction (STEMI), 1 enrolled patients with non-STEMI only, and 1 enrolled patients with all types of ACS. There was no difference between ICR and staged CR regarding all-cause and cardiovascular mortality at any time window. ICR reduced the rate of myocardial infarction and decreased the rate of repeat revascularization at 6 months and beyond. The rates of cerebrovascular events and stent thrombosis were similar between the 2 groups. In conclusion, the present meta-analysis demonstrated a lower rate of myocardial infarction and a reduction in repeat revascularization at and after 6 months with ICR strategy in patients with mainly STEMI and MVD. The 2 groups had no difference in the risk of all-cause and cardiovascular mortality. Further RCTs are needed to provide more definitive conclusions and investigate CR strategies in other ACS.

Response to: regarding the significance of anti-COVID-IgA antibody response in COVID-19 breakthrough infection.

In response to Chen et al.'s comments on our paper regarding the significance of anti-COVID-IgA antibody response in COVID-19 breakthrough infection in vaccinated patients, we have highlighted the role and the scope of this paper in this correspondence. The role of anti-COVID-19-IgA is already known. The objective of the previous study was to see its role in breakthrough-infected patients. To analyse this effect, we recruited patients with COVID-19 infection after they were fully vaccinated and compared them with the vaccinated group who did not get the infection. Both groups were equally exposed to the virus as all of them were health care workers. We also showed that the anti-COVID-19-NP-IgA was absent in the healthy cohort of our study groups, signifying the absence of natural infection in them during this period. The article also highlights the importance of vaccinating all individuals including those who are immunosuppressed, as it prevents severe COVID-19 infection in these individuals. The physicians should be aware of the fact that immunosuppressed patients are more likely to get COVID-19 breakthrough infection. However, proper vaccination with booster doses prevents severe infection in them.

Significance of Anti-COVID-IgA antibody response in COVID-19 breakthrough infection in vaccinated patients - a single-centered study from Pakistan.

An increasing number of breakthrough-COVID-19-vaccinated individuals are being reported across the world. Humoral immunity has a crucial role in combating infection. In this study, we aimed to assess the importance of anti-COVID-S1-IgA and anti-COVID-NP-IgA in confirmed COVID-19 after vaccination (breakthrough infection group). Blood samples were collected from the breakthrough infection group within one week of breakthrough infections (n = 34). A second sample was also collected after 4 to 8 weeks (n = 27). Blood samples of healthy individuals (n = 29) were collected 4-8 weeks after the completion of vaccination. Anti-COVID-S1-IgA and anti-COVID-NP-IgA were detected by ELISA. Statistical analysis was performed using IBM SPSS version 24. In this study, we found a higher positivity rate for anti-COVID-S1-IgA in the breakthrough infection group (70% vs. 28% in healthy individuals). Anti-COVID-NP-IgA was not found in the control group (11% in the breakthrough infection group vs. 0 in healthy individuals). In the breakthrough-infected group, the positivity rate of anti-COVID-NP-IgA decreased significantly (median titers 16.9 IU/ml decreased to 4.2 IU/ml) p = 0.001), while anti-COVID-S1-IgA increased over a period of 4-8 weeks (9.35-16.35 IU/ml). Importantly, IgA response to both COVID-19 NP and S1 antigens was not found in 13 patients at initial testing. The findings of this study show that serum IgA may have a role both in breakthrough infections and also in the prevention of severe infection. Sluggish anti-COVID-19-IgA antibody response may be responsible for the occurrence of COVID-19 infection in breakthrough infection. On the other hand, more sustained anti-COVID-19-S1-IgA over a longer period of time may have a role in preventing these patients from severe infections and hospitalization. However, a study on a larger sample size including patients with severe disease after vaccination is required to prove this hypothesis. To the best of our knowledge, this is the first study reporting the importance of serum IgA in breakthrough-infected patients from our region.

South Asian medical cohorts reveal strong founder effects and high rates of homozygosity.

The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies.

Role of Human Papillomavirus in Various Cancers: Epidemiology, Screening and Prevention.

It is estimated that viral infections contribute 15-20% of all human cancers. Several types of human papillomaviruse (HPV) are related to the development of many cancer types and their treatment. About 200 HPV viruses have been identified, and each type of virus is integrated with a certain type of clinical lesions affecting the areas of skin and mucous membranes. Infections associated with HPV are known to cause multiple cancer types such as uterine cancer, cervical, vaginal carcinoma, and other varieties of carcinomas such as genital and oropharyngeal, penile, and short-lived carcinomas. Most of the HPV types are considered as high-risk mutants that can provoke cervical cancer in females in addition to significant contribution to other cancer types such as anogenital cancer and tumor in head and neck regions. Among them, high risk human papilloma viruses (HR-HPV) subtypes 16 and 18 play a major role in the etiology of cervical cancer worldwide. Although, cervical cancer incidence and fatality rates vary greatly depending on geographical area, it is the leading cause of mortality in women around the world. In addition, it is epidemiologically similar to a sexually transmitted disease of low infectivity. In this review article, the association of HPV with different types of cancers have been explained, but the main focus remains on cervical cancer.

Quercetin for the treatment of COVID-19 patients: A systematic review and meta-analysis.

Currently approved therapies for COVID-19 are mostly limited by their low availability, high costs or the requirement of parenteral administration by trained medical personnel in an in-hospital setting. Quercetin is a cheap and easily accessible therapeutic option for COVID-19 patients. However, it has not been evaluated in a systematic review until now. We aimed to conduct a meta-analysis to assess the effect of quercetin on clinical outcomes in COVID-19 patients. Various databases including PubMed, the Cochrane Library and Embase were searched from inception until 5 October 2022 and results from six randomized controlled trials (RCTs) were pooled using a random-effects model. All analyses were conducted using RevMan 5.4 with odds ratio (OR) as the effect measure. Quercetin decreased the risk of intensive care unit admission (OR = 0.31; 95% confidence interval (CI) 0.10-0.99) and the incidence of hospitalisation (OR = 0.25; 95% CI 0.10-0.62) but did not decrease the risk of all-cause mortality and the rate of no recovery. Quercetin may be of benefit in COVID-19 patients, especially if administered in its phytosome formulation which greatly enhances its bioavailability but large-scale RCTs are needed to confirm these findings.

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach.

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.

ANA-specific antibodies, ANA patterns, anti-ds-DNA results, and clinical diagnosis: a laboratory and clinical audit.

The diagnosis of systemic autoimmune diseases (SAID) is challenging, due to overlapping features with other non-immune disorders. Anti-nuclear antibodies (ANA)/anti-cellular antibodies are the sensitive screening tests but anti-double-stranded-deoxyribonucleic acid-antibody (anti-ds-DNA) and ANA-specific antibodies are specific for SAID. We aimed to look at ANA-specific antibodies in our patients and correlated them with ANA patterns, anti-ds-DNA, and clinical diagnosis for proper interpretation and better patient management cost-effectively. A retrospective data analysis of 641 patients was done (1st of February 2019 to 31st of July 2021) who were tested for ANA-specific antibodies at the Immunology Department of Indus Hospital and Health Network. ANA and anti-ds-DNA results and clinical diagnosis were also analyzed for ANA-specific antibody-positive patients. Descriptive data were presented in mean ± standard deviation and frequency percentages whereas inferential data were analyzed with a chi-square test for association between ANA-specific antibodies status, ANA, anti-ds-DNA, and clinical features. ANA-specific antibodies test revealed positivity for at least one autoantibody in 245 (38.2%) patients. Of these, ANA was tested in 206 patients reactive for ANA-specific antibodies and found positive in 195 (95%) as compared to negative (< 0.001). Speckled and homogenous were predominant ANA patterns in ANA-specific antibody-positives (56% and 42% respectively). Multiple ANA patterns were found in 18 patients most commonly with systemic lupus erythematosus (SLE) and mixed connective tissue disorder (MCTD). Anti-SSA were the most common ANA-specific antibodies (50%) and were mostly found in sera with speckled (61/97) and homogenous (38/97) patterns and associated mostly with SLE (48%) and Sjogren's syndrome (86%). Among ANA-negative patients, anti-SSA were the most common antibodies (n = 5). Anti-ds-DNA was found in 66% of SLE patients along with another ANA-specific antibody. This study showed that testing for ANA-specific antibodies cannot be gated on ANA patterns. Also, there is a redundancy of these antibodies with various clinical diagnoses. Moreover, they are useful in making a diagnosis in ANA-negative patients as well with clinical suspicion.

Cytotoxic and genotoxic evaluation of bisphenol S on onion root tips by Allium cepa and comet tests.

Bisphenol S (BPS) is an analog of bisphenol A, which is used as substitute of BPA in many products like airport luggage tags, baby bottles, plastics, and epoxy resins etc. Bisphenol S can cause toxic effects in different organisms, i.e., mice, rat, zebrafish, and C.elegans, etc. Bisphenol S is also known as "endocrine disruptor" due to its ability to mimic the endocrine receptors. So, the aim of this study was to evaluate the cytotoxic and genotoxic effects of bisphenol S on meristematic cells present in onion root tips through Allium cepa (A.cepa) and comet tests. Root growth inhibition was evaluated by root growth inhibition assay. Mitotic index (MI) and chromosomal aberrations (CAs) were assessed by A.cepa assay. DNA damage was evaluated by comet assay. Root growth of A.cepa was inhibited due to bisphenol S. LC50 value calculated by root growth inhibition assay for bisphenol S was (2.6±0.63, 50 µg/ml). Mitotic index was reduced, and chromosomal aberrations were observed, i.e., stickiness, polyploidy, and disturbed ana-telophase in anaphase and telophase stages of mitosis. In case of comet assay, DNA damage was increased in statistically significant manner (p ≤ 0.05). It was concluded that bisphenol S constitutes cytotoxic and genotoxic effects on A. cepa root meristematic cells. Moreover, it is suggested to explore more toxicity studies of bisphenol S at molecular level.

An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique.

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.

Copy Number Profiling of MammaPrint™ Genes Reveals Association with the Prognosis of Breast Cancer Patients.

PURPOSE: The MammaPrint™ gene signature, currently used in clinical practice, provides prognostic information regarding the recurrence and potential metastasis in breast cancer patients. However, the prognostic information of the 70 genes included can only be estimated at the RNA expression level. In this study, we investigated whether copy number information of MammaPrint™ genes at the DNA level can be used as a prognostic tool for breast cancer, as copy number variations (CNVs) are major contributors to cancer progression. METHODS: We performed CNV profiling of MammaPrint™ genes in 59 breast cancer cell lines and 650 breast cancer patients, using publicly available data in The Cancer Genome Atlas (TCGA) database. Statistical analyses including Fisher exact test, chi-square test, and Kaplan-Meier survival analyses were performed. RESULTS: All MammaPrint™ genes showed recurrent CNVs, particularly in TCGA cohort. CNVs of 32 and 36 genes showed significant associations with progesterone receptor and estrogen rector, respectively. No genes showed a significant association with human epidermal growth factor receptor 2 status and lymph node status. In addition, only six genes were associated with tumor stages. RFC4, HRASLS, NMU, GPR126, SCUBE2, C20orf46, and EBF4 were associated with reduced survival and RASSF7 and ESM1 were associated with reduced disease-free survival. CONCLUSION: Based on these findings, a concordance of CNV-based genomic rearrangement with expression profiling of these genes and their putative roles in disease tumorigenesis was established. The results suggested that the CNV profiles of the MammaPrint™ genes can be used to predict the prognosis of breast cancer patients. In addition, this approach may lead to the development of new cancer biomarkers at the DNA level.