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Abstract The growing preference for natural remedies has resulted in increased use of medicinal plants. One of the most significant and varied plants is garden cress (Lepidium sativum), which has large concentrations of proteins, fatty acids, minerals, and vitamins. It also contains a wide range of bioactive components, including kaempferol glucuronide, gallic acid, protocatechuic acid, coumaric acid, caffeic acid, terpenes, glucosinolates, and many more. These substances, which include antioxidant, thermogenic, depurative, ophthalmic, antiscorbutic, antianemic, diuretic, tonic, laxative, galactogogue, aphrodisiac, rubefacient, and emmengogue qualities, add to the medicinal and functional potential of garden cress. An extensive summary of the phytochemical profile and biological activity of garden cress seeds is the main goal of this review. Research showed that garden cress is one of the world's most underutilized crops, even with its nutritional and functional profile. Consequently, the goal of this review is to highlight the chemical and nutritional makeup of Lepidium sativum while paying particular attention to its bioactive profile, various health claims, therapeutic benefits, and industrial applications.

Abstract According to an estimate, 30% to 40%, of global fruit are wasted, leading to post harvest losses and contributing to economic losses ranging from $10 to $100 billion worldwide. Among, all fruits the discarded portion of oranges is around 20%. A novel and value addition approach to utilize the orange peels is in nanoscience. In the present study, a synthesis approach was conducted to prepare the metallic nanoparticles (copper and silver); by utilizing food waste (Citrus plant peels) as bioactive reductants. In addition, the Citrus sinensis extracts showed the reducing activity against metallic salts copper chloride and silver nitrate to form Cu-NPs (copper nanoparticles) and Ag-NPs (Silver nanoparticles). The in vitro potential of both types of prepared nanoparticles was examined against plant pathogenic bacteria Erwinia carotovora (Pectobacterium carotovorum) and pathogens effect on human health Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Moreover, the in vivo antagonistic potential of both types of prepared nanoparticles was examined by their interaction with against plant (potato slices). Furthermore, additional antipathogenic (antiviral and antifungal) properties were also examined. The statistical analysis was done to explain the level of significance and antipathogenic effectiveness among synthesized Ag-NPs and Cu-NPs. The surface morphology, elemental description and size of particles were analyzed by scanning electron microscopy, transmission electron microscopy, energy-dispersive spectroscopy and zeta sizer (in addition polydispersity index and zeta potential). The justification for the preparation of particles was done by UV–Vis Spectroscopy (excitation peaks at 339 nm for copper and 415 nm for silver) and crystalline nature was observed by X-ray diffraction. Hence, the prepared particles are quite effective against soft rot pathogens in plants and can also be used effectively in some other multifunctional applications such as bioactive sport wear, surgical gowns, bioactive bandages and wrist or knee compression bandages, etc.

Background: Visual impairment creates adverse effects on the affected individual and on his family. Speech therapists played an essential role in the development of children who had communication problems. Objective: To explore the perceptions of special education teachers and speech pathologists about role of speech therapists in children with visual impairment. Methodology: A qualitative study on exploring the role of speech pathologists in visual impairment was conducted in Rawalpindi and Islamabad region in 2022. Purposive sampling was used to include 8 speech therapists and 7 special education teachers. Speech pathologists having minimum qualification of MS/SLP or PGD and Special education teachers with minimum qualification of master’s in special education with a minimum two years of experience were included. Open-ended questions were developed, and data was gathered by using semi-structured interviews. Recordings were done after informed consent. Thematic analysis was done to generate the themes. Results: Four major themes were identified by both professionals. These include 1) the need for guidance for professionals 2) auditory and tactile orientations for the development of language 3) scarce resources in therapy, 4) the need for training for professionals who are working with visually impaired children. Conclusion: Speech therapists had a vital role in developing communication of children with visual impairment. Professionals suggested that speech therapists should work with children with VI as they work with sighted children.

Lahore (Pakistan), being an industrial city, has high emission of aerosols that affects and contaminates the air quality. Therefore, the abatement/inactivation of aerosols is necessary to restrict their infectious activities. In this project, ionic wind isolated from dielectric barrier discharge plasma (DBD plasma) has been utilized to abate the aerosols trapped in the Surgical Mask and KN95 Respirator. To infer the chemical and elemental detection of ambient aerosols, FTIR and LIBS have been employed. “From the results, it is noteworthy that abatement/removal of aerosols has been successfully carried out by the ionic wind irradiation and highlights the potential of DBD plasma technology in removing the aerosols pollution.”

Opisthorchis viverrini is the etiological agent of the disease opisthorchiasis and related cholangiocarcinoma (CCA). It infects fish-eating mammals and more than 10 million people in Southeast Asia suffered from opisthorchiasis with a high fatality rate. The only effective drug against this parasite is Praziquantel, which has significant side effects. Due to the lack of appropriate treatment options and the high death rate, there is a dire need to develop novel therapies against this pathogen. In this study, we designed a multi-epitope chimeric vaccine design against O. viverrini by using immunoinformatics approaches. Non-allergenic and immunogenic MHC-1, MHC-2, and B cell epitopes of three candidate proteins thioredoxin peroxidase (Ov-TPx-1), cathepsin F1 (Ov-CF-1) and calreticulin (Ov-CALR) of O. viverrini, were predicted to construct a potent multiepitope vaccine. The coverage of the HLA-alleles of these selected epitopes was determined globally. Four vaccine constructs made by different adjuvants and linkers were evaluated in the context of their physicochemical properties, antigenicity, and allergenicity. Protein-protein docking and MD simulation found that vaccines 3 was more stable and had a higher binding affinity for TLR2 and TLR4 immune receptors. In-silico restriction cloning of vaccine model led to the formation of plasmid constructs for expression in a suitable host. Finally, the immune simulation showed strong immunological reactions to the engineered vaccine. These findings suggest that the final vaccine construct has the potential to be validated by in vivo and in vitro experiments to confirm its efficacy against the CCA causing O. viverrini.

Abstract 3D seismic attributes analysis can help geologists and mine developers associate subsurface geological features, structures, faults, and ore bodies more precisely and accurately. The major influence of this application is to evaluate the usage of the 3D seismic attributes analysis in gold mine planning. For this, we evaluate the novel theory of complex T-spherical hesitant fuzzy (CTSHF) sets and their operational laws. Furthermore, we derive the CTSHF Aczel–Alsina weighted power averaging (CTSHFAAWPA) operator, CTSHF Aczel–Alsina ordered weighted power averaging (CTSHFAAOWPA) operator, CTSHF Aczel–Alsina weighted power geometric (CTSHFAAWPG) operator, and CTSHF Aczel–Alsina ordered.com weighted power geometric (CTSHFAAOWPG) operator. Some properties are also investigated for the above operators. Additionally, we evaluate the problems of 3D seismic attributes analysis to mine planning under the consideration of the proposed operators, for this, we illustrate the problem of the multi-attribute decision-making (MADM) technique for the above operators. Finally, we demonstrate some examples for making the comparison between prevailing and proposed information to improve the worth of the derived operators.

Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.

Industrial parts are usually shaped with complex surface structures and they require high surface quality. The surface defects influence the precision and performance of them. The efficient optical inspection equipment and defect detection and classification method are important for improving t productivity. There are two main difficulties in the visual detection of surface defects of industrial parts. One is difficult to develop visual inspection equipment for complex surfaces. Second is that the optimal feature set extracted by the commonly used feature fusion classification methods is not sensitive to defects with high defect similarity, which affects the defect classification accuracy. To solve difficulty 1, We have researched the principles of refraction and reflection when a light beam reaches the interface of a non-homogeneous medium, and develop a high-performance optical inspection equipment, which is available for 360° image acquisition of the external surface of an industrial part. To solve difficulty 2, a feature fusion classification method based on defect similarity measurement is proposed. The main idea is to measure the similarity between various types of defects by calculating the Euclidean distance between class centers and to classify defects with similarity higher than a threshold into one category. Subsequently, a two-step feature extraction and classification strategy is adopted. In the first step, the lower similarity types are separated, and then in the second step, the higher similarity types are separated. Deep learning methods and traditional machine learning methods are used for validation, respectively. Experimental results indicate that the proposed method achieves a 5% improvement in overall classification accuracy, and also has significant improvements in precision, recall and many other indicators compared to several other advanced classification methods.

Background: The geo-strategic position of Pakistan on the world map is incredibly important and idyllic as the country is considered the gateway to central Asia. Pakistan has faced political instability for the last three decades, causing a brain drain and adversely affecting socioeconomic growth. This study aims to investigate the impact of an unstable environment on the brain drain of highly skilled professionals, healthcare workers, researchers, and research productivity in Pakistan from January 2000 to December 2022. Material and Methods: The data were recorded from the World Bank, the Higher Education Commission (HEC) Pakistan, the Pakistan Medical and Dental Council (PMDC), the Bureau of Emigration and Overseas Employment (BEOS), Pakistan, Academic Ranking of World Universities (ARWU), and Web of Science Clarivate Analytics. Initially, 32 documents were selected in this study, and finally, eight fact sheets, official government websites, and international organizations were included. Results: The result revealed that due to political instability, in 2022 about 832,339 highly qualified and accomplished experts headed abroad, among them 17976 (2.15%) were highly qualified and 20865 (2.50%) were highly competent professionals. These include accountants 7197 (0.86%), engineers 6,093 (0.73%), agricultural experts 3,110 (0.37%), doctors 2,464 (0.29%), computer experts 2,147 (0.25%), nurses and paramedics 1768 (0.21%), technicians 23347 (2.80%), electricians 20322 (2.44%), and schools and university faculty 1004 (0.12%). Pakistan has a total of 380 Higher Education Commission-indexed academic journals, among them 11 (2.89%) academic journals were indexed in the Web of Science and 23 journals were placed in the Web of Science emerging indexing. Among these journals, only one journal surpassed the impact factor of more than 2.0. The quartile ranking of Pakistani journals is 01 journal in Q2; 02 in Q3; and the remaining 08 journals in Q4. From August 1947 to December 2022, Pakistan produced a total of 259249 research articles, and from January 2000 to December 2022, the number of articles published was 248457 (95.83%). Since the last 22 years, the trend of research publications was continuously increased; however, the rising trend decreased in 2022 with a declined rate of 1263 (3.42%). Conclusion: The unstable sociopolitical environment in Pakistan caused a brain drain of highly qualified and skilled professionals and impaired the global standing of universities, academic journals, and research productivity in Pakistan. Pakistan must resolve the instability and establish sustainable policies to minimize the brain drain of highly qualified and skilled experts and convalesce their academic institutes and their research productivity for the development of the nation.

Abstract Background Erector spinae plane block (ESPB) and serratus anterior plane block (SAPB) are regional anesthesia techniques that have shown favorable results in pain management following thoracic surgeries; however, their relative superiority is unclear. This review (PROSPERO: CRD42023443018) aims to compare the analgesic efficacy of ESPB and SAPB in patients undergoing thoracic surgeries through the pooled analysis of co-primary outcomes: postoperative oral-morphine-equivalent (mg) consumption in 24 h and pain scores (static) at 24 h. Methods A literature search was conducted across PubMed, Cochrane Library, and Google Scholar to identify randomized controlled trials (RCTs) from inception to May 2023, comparing ESPB and SAPB in thoracic surgeries. Statistical pooling was done using Review Manager 5.4.1. Bias assessment employed the Cochrane Collaboration Risk-of-Bias 2.0 tool. The strength of evidence was assessed using the guidelines from the GRADE working group. Results Nine RCTs (485 patients) were included in the study. Postoperative pain scores (static) at 24 h (mean difference (MD) = − 0.31 [− 0.57, 0.05], p = 0.02) and postoperative oral-morphine-equivalent (mg) consumption in 24 h (MD = − 19.73 [− 25.65, − 13.80], p

Abstract Background Females have historically lower rates of cardiovascular testing when compared to males. Clinical decision pathways (CDP) that utilize standardized risk-stratification methods may balance this disparity. We sought to determine whether clinical decision pathways could minimize sex-based differences in the non-invasive workup of chest pain in the emergency department (ED). Moreover, we evaluated whether the HEART score would minimize sex-based differences in risk-stratification. Methods We conducted a retrospective cohort review of adult ED encounters for chest pain where CDP was employed. Primary outcome was any occurrence of non-invasive imaging (coronary CTA, stress imaging), invasive testing, intervention (PCI or CABG), or death. Secondary outcomes were 30-day major adverse cardiac events (MACE). We stratified HEART scores and primary/secondary outcomes by sex. Results A total of 1078 charts met criteria for review. Mean age at presentation was 59 years. Females represented 47% of the population. Low, intermediate, and high-risk patients as determined by the HEART score were 17%, 65%, and 18% of the population, respectively, without any significant differences between males and females. Non-invasive testing was similar between males and females when stratified by risk. Males categorized as high risk underwent more coronary angiogram (33% vs. 16%, p = 0.01) and PCI (18% vs. 8%, p = 0.04) than high risk females, but this was not seen in patients categorized as low or intermediate risk. Males experienced more MACE than females (8% vs. 3%, p = 0.001). Conclusions We identified no sex-based differences in risk-stratification or non-invasive testing when the CDP was used. High risk males, however, underwent more coronary angiogram and PCI than high risk females, and consequently males experienced more overall MACE than females. This disparity may be explained by sex-based differences in the pathophysiology driving each patient’s presentation.

Background: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) worsens the respiratory symptoms that are usually triggered by infection with bacteria or viruses or by environmental pollutants. Therefore, the aim of this study was to determine the bacterial etiology from sputum culture in patients suffering from acute exacerbation of COPD, admitted in hospital. Methods: The study was cross sectional observational, where sputum bacterial cultures were analyzed from the patients with Acute exacerbation COPD treated in the ICU of a tertiary care hospital for pulmonary disease from January 2019 to December 2019. Two sputum samples were collected from each patient for bacterial examination. The results of sputum bacterial culture findings were expressed as frequency and percentage by using SPSS. Results: In the present study, there were 1296 patients, both males 749 (57.8%) and females 547 (42.2%). The mean age of patients was 57.39±19.74years. 470 (36.3%) showed negative culture reports. 440 (34.0%) patients had Moraxella infection, which was most common organism in all patients, and 149 (11.5%) patients had Pseudomonas infection, 157(12.1%) patients had Yeast Albicans and in only 1 (0.1%) patient Enterobacter infection were found. Other pathogens in low frequency identified were Haemophilus parainfluenzae, Streptococcus pneumoniae, Escherichia coli and Haemophilus influenzae. It was observed that the frequency of infections was linked with increasing age. Conclusion: With increasing age, people are prone to acquire pulmonary infections specifically COPD. It is therefore very important to perform sputum culture to identify the causative agents and treat the patients with appropriate antibiotic to reduce the episodes of AECOPD. Keywords: Bacteria; Sputum Culture; COPD; Antibiotic; Cough.

Severe acute respiratory syndrome corona virus 2 (SARS CoV-2) is the cause of Corona virus disease 2019 (COVID-19), which turned into a pandemic in late 2019 and early 2020. SARS CoV-2 causes endothelial cell destruction and swelling, microthrombosis, constriction of capillaries, and malfunction of pericytes, all of which are detrimental to capillary integrity, angiogenesis, and healing processes. Cytokine storming has been connected to COVID-19 disease. Hypoxemia and tissue hypoxia may arise from impaired oxygen diffusion exchange in the lungs due to capillary damage and congestion. This personal view will look at how inflammation and capillary damage affect blood and tissue oxygenation, cognitive function, and the duration and intensity of COVID-19 disease. The general effects of microvascular injury, hypoxia, and capillary damage caused by COVID-19 in key organs are also covered in this point of view. Once initiated, this vicious cycle leads to diminished capillary function, which exacerbates inflammation and tissue damage, and increased inflammation due to hypoxia. Brain damage may result from low oxygen levels and high cytokines in brain tissue. In this paper we give a summary in this direction with focus on the role of the neuropeptide Substance P. On the basis of this, we discuss selected approaches to the question: “How Substance P is involved in the etiology of the COVID-19 and how results of our research could improve the prevention or therapy of corona? Thereby pointing out the role of Substance P in the post-corona syndrome and providing novel concepts for therapy and prevention.

Topological indices (TIs) have rich applications in various biological contexts, particularly in therapeutic strategies for cancer. Predicting the performance of compounds in the treatment of cancer is one such application, wherein TIs offer insights into the molecular structures and related properties of compounds. By examining, various compounds exhibit different degree-based TIs, analysts can pinpoint the treatments that are most efficient for specific types of cancer. This paper specifically delves into the topological indices (TIs) implementations in forecasting the biological and physical attributes of innovative compounds utilized in addressing cancer through therapeutic interventions. The analysis being conducted to derivatives of sulfonamides, namely, 4-[(2,4-dichlorophenylsulfonamido)methyl]cyclohexanecarboxylic acid (1), ethyl 4-[(naphthalene-2-sulfonamido)methyl]cyclohexanecarboxylate (2), ethyl 4-[(2,5-dichlorophenylsulfonamido)methyl]cyclohexanecarboxylate (3), 4-[(naphthalene-2-sulfonamido)methyl]cyclohexane-1-carboxylic acid (4) and (2S)-3-methyl-2-(naphthalene-1-sulfonamido)-butanoic acid (5), is performed by utilizing edge partitioning for the computation of degree-based graph descriptors. Subsequently, a linear regression-based model is established to forecast characteristics, like, melting point and formula weight in a quantitative structure-property relationship. The outcomes emphasize the effectiveness or capability of topological indices as a valuable asset for inventing and creating of compounds within the realm of cancer therapy.

Objective: To compare the mean facial soft tissue thickness between males and females in different malocclusion groups. Study Design: Cross-Sectional Study. Place and Duration of Study: Armed Forces Institute of Dentistry, Rawalpindi Pakistan, from Jan 2020 to Jan 2021. Methodology: Cephalometric radiographs of 230 patients were used to measure soft tissue thickness at seven landmarks: the glabella, subnasal region, labrale superius, labrale inferius, sulcus labrale superius, labiomentalis, and soft tissue chin. Results: Of 230 patients, 39% were of Class I, 21% of Class II/1, 26% of Class II/2 and 13% of Class III. The gender ratio was the same in all skeletal classes. The mean age of 230 patients was 18.36±2.29 years. The mean ANB angle and UI were 4.02±3.22 and 25.95±8.86. The mean ANB angle and UI significantly differed between skeletal classes. In contrast, the mean age of patients of different skeletal classes was not significantly different, with a p-value of 0.433. The mean FSTT measured from subnasal area (A-NS), sulcus labrale superius (RR-SLS), labrale superius (J-LS), labrale inferius (I-Li) and chin (Pg-Pg1) was significantly different between skeletal classes (p value

Breast cancer accounts for one in three new cancer cases in women each year. Despite having a higher survival rate than other cancers, it is associated with various side effects, including anorgasmia, vaginismus, hair loss, and decreased libido. This review aims to explore trends in the incidence of sexual dysfunction in breast cancer survivors, the etiology of sexual dysfunction, and the role of factors such as family history, age, duration of marriage, and depression in predisposing patients. We summarize the limitations of the treatment modalities already used to cater to sexual dysfunction in breast cancer survivors and patients. The authors conducted searches on databases such as PubMed and Google Scholar using relevant search terms: sexual dysfunction, breast cancer, breast cancer survivors, chemotherapy, dyspareunia, vaginismus, and anorgasmia from 1997–2023. The inclusion criteria encompassed all types of articles with abstracts or titles indicating research on sexual dysfunction in breast cancer survivors in Asia. A total of 64 articles were included out of which 10 were systematic reviews and meta-analyses. The literature search yielded results showing high incidence rates of breast cancer in Asia (45.4%), with 31.6%–91.2% of breast cancer survivors likely to experience sexual dysfunction. Regional differences were noted, as female sexual dysfunction occurred in 74.1% of Asian breast cancer women. Further randomized controlled trials should be conducted to assess the effectiveness of treatment modalities. Personalized approaches should be tailored to address beliefs, such as the potential impact of sexual activity on disease recovery. Utilizing a family history of breast cancer as a preemptive tool can help reduce the risk of developing female sexual dysfunction in survivors, and factors such as age and depression should be considered when formulating solutions.

Schweizer and Sklar invented the idea of the Schweizer-Sklar t-norm and Schweizer-Sklar t-conorm in 1960. Additionally, prioritized aggregation operators were derived by Yager in 2008. Further, linear Diophantine fuzzy (LDF) information is a most recent generalization of q-rung orthopair, Pythagorean, and intuitionistic fuzzy sets. In this article, we invent the Schweizer-Sklar operational laws for LDF set based on the Schweizer-Sklar t-norm and t-conorm. Furthermore, we expose the LDF Schweizer-Sklar prioritized averaging (LDFSSPoA) operator, LDF Schweizer-Sklar weighted prioritized averaging (LDFSSWPoA) operator, LDF Schweizer-Sklar prioritized geometric (LDFSSPoG) operator, LDF Schweizer-Sklar weighted prioritized geometric (LDFSSWPoG) operator and evaluate their fundamental properties and results. Moreover, after evaluation of these operators, we evaluate the problem of the green sustainable chain to enhance the worth of the invented theory. Finally, we show the supremacy and accuracy of the proposed theory with the help of a comparative analysis between the proposed techniques with some existing techniques.

ABSTRACTDiabetic peripheral neuropathy is one of the most prevalent complications of diabetes mellitus. It is the most common type of neuropathy characterized by decreased sensory functions in the lower extremities and substantial neuropathic pain. Based on clinical characterization, it is classified into symmetric and asymmetric neuropathy. The pathological changes and neuronal function impairment during diabetes are associated with various pathways, including polyol pathway activation, advanced glycation end-products formation, oxidative stress, protein kinase C activation, poly ADP-ribose polymerase, and hexosamine pathways. Demyelination, axonal atrophy, nerve fibre loss, reduced regeneration, and loss of neurovascular interactions are hallmarks. Although some symptomatic and supportive therapies, such as tricyclic agents, antiarrhythmics, opioid analgesics, incretin, aldose reductase, and protein kinase C inhibitors, are in practice, the outcome is not promising. To fill this gap, natural product-based therapy can prove prodigious as an effective alternative. This review aims to comprehend the available literature on the role of various biological molecules in ameliorating diabetic peripheral neuropathy. These molecules play a key role in reducing oxidative-nitrosative stress, aldose reductase activity, and neuronal apoptosis. They control glucose and HbA1c% levels and improve nerve conduction velocity, axonal regeneration, and antioxidant species (catalase, superoxide dismutase, malondialdehyde). They are known for their attenuating thermal and mechanical hyperalgesia and tactile allodynia. Therefore, there is a need to evaluate these molecules at the pre-clinical and clinical levels for efficacy. Hence, natural molecules may act as promising players against diabetic peripheral neuropathy and are a ray of hope for suffering individuals.

Background and Objectives: Acinetobacter baumannii (A. baumannii), particularly carbapenem-resistant A. baumannii (CRAB), represents a grave concern in healthcare settings and is associated with high mortality. This study aimed to conduct molecular, mutational, and phylogenetic analyses of specific genes in CRAB and evaluate the synergistic effects of selected antimicrobial combinations. Materials and Methods: Phenotypic characterization was performed on six CRAB strains by using the Modified Hodge Test (MHT) and IMP-EDTA Double-Disc Synergy Test (IMP-EDTA DDST). Carbapenemase- and metallo-beta-lactamase-encoding genes were amplified by using Polymerase Chain Reaction. Phylogenetic analysis using the MEGA 11 tool was used to determine the evolutionary relatedness of these genes. Mutational analysis was performed by using I-Mutant, MUPro, and PHD-SNP bioinformatics tools to predict mutations in the carbapenemase-encoding genes. Microdilution checkerboard titration assessed the synergistic effects of antimicrobial combinations (azithromycin–meropenem, rifampicin–meropenem, meropenem–colistin, and azithromycin–colistin) on these CRAB isolates. Results: The phenotypic characterization of six CRAB isolates revealed positive results for MHT and IMP-EDTA DDST. The molecular characterization revealed that carbapenemase- and MBL-encoding genes were present in all isolates with varying frequencies, including blaOXA-51 (100%) and blaIMP (0%). The sequence analysis revealed high evolutionary relatedness to sequences in the NCBI database. The mutational analysis identified 16 mutations, of which 1 mutation (P116L) in the blaOXA-58 gene predicted a change in the protein product, potentially contributing to carbapenem resistance. The checkerboard titration method did not reveal any synergism among the tested antimicrobial combinations against CRAB. Conclusion: This study’s findings underscore the significant challenges posed by CRAB isolates harboring multiple resistant genes in treatment. This highlights the urgent need for novel antimicrobial agents, a crucial step towards reducing mortality rates not only in Pakistan but also globally.

Objective: To compare various labour parameters in partogram among primiparous women and women with one previous caesarean section scar Study Design: Comparative cross-sectional study Place and Duration of Study: Gynaecology and Obstetrics Department, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Oct 2021 to Sep 2022. Methodology: This study was conducted on primiparous women and women with one previous scar who were booked cases in our department for antenatal checkups and labour. Patients were divided into two groups for comparison. Group-I were primiparous, while Group-II had one previous scar. All women underwent detailed labour records via conventional partogram in the labour room. Duration of the active phase of labour, duration of the second stage, time to progress by 1 cm and time after the alert line were compared in partograms of both the study groups. Results: A total of 390 women who were either primiparous or had one previous caesarean section were recruited for this study. Of the study participants, 226(42.6%) were primiparous, while 164(57.4%) had one previous caesarean section scar. Statistical analysis revealed that the duration of the active phase of labour, time to progress by 1 cm and time after the alert line were statistically significant (p-value

Hybrid nanofluids (HNFs) of metallic oxide-based nanoparticles (NPs) have been prepared in different basefluids (BFs) employing the thermal plasma technique. NPs of ZnO–MgO were directly dispersed into pristine coolant, engine oil, distilled water (DW), and coconut oil. Plasma was generated between two identical electrodes applying 8.0 kV at the ambient conditions and proved economically viable in preparing stable HNFs. X-ray Diffractometry (XRD) showed ZnO and MgO NPs possessed hexagonal and cubic crystal structures, respectively. The band gap is calculated through UV-visible spectroscopy. The thermal conductivity (TC) of the HNFs has been measured using a thermal conductivity analyzer based on the transient hot wire method. The band gaps of pristine coolant and its HNFs were obtained to be 3.35 eV and 3.33 eV, respectively. In engine oil and its HNFs, band gaps of 3.16 eV and 3.02 eV have been extracted. There appears to be a slight reduction in band gap for coolant and engine oil-based HNFs. The band gap value of coconut oil-based HNFs was 4.05 eV, which showed a higher value than the pristine coconut oil-based HNFs (3.95 eV). The band gap calculated in the case of DW-based HNFs was 3.79 eV. TC of HNFs with volume concentration of 0.019 % for DW, 0.020 % for coolant, 0.016 % for engine oil, and 0.017 % for coconut oil were tested between 20 and 60 °C. An increase in TC was observed with the rise in temperature of the HNFs. Maximum increment in TC was observed at 60 °C for coolant-based HNFs, which was 19 %, followed by DW (18%), coconut oil (18%), and engine oil (16%), respectively. DW-based HNFs can be used as a coolant and optical filter for optoelectronics devices like photovoltaic cells for better performance. The study underscores precise control of NPs size as pivotal for band gap influence. HNFs hold promise as the next-gen heat transfer fluids (HTFs), revolutionizing thermal conductivity across industries. This research lays a firm foundation for plasma-synthesized HNFs' application in enhanced heat transfer and optoelectronic devices. Coolant-based HNFs excel in thermal conductivity, addressing heat transfer challenges.

This article is an overview of the current state of nanoparticles because of their emerging usage in cancer treatment, covering nanoparticles that have been authorized for use in cancer therapy currently undergoing clinical testing. Recent advances in nanoparticle engineering, coupled with an enhanced understanding of critical nanoparticle attributes (size, shape, and surface properties) in conjunction with biological systems, present novel prospects for therapeutic nanoparticle development. Although inorganic and metallic nanoparticles are gaining recognition in clinical studies due to their potential usefulness but nanomaterials primarily polymeric, liposomal, and nano crystal based dominate the cancer therapy. Polymeric nanoparticles (NPs) contain specific ligands, such as polyglycolic acid (PGA) and polylactic acid (PLA), which have a chemical affinity for malignant cells and target tumors. Nanocrystals stand out with their high loading efficiency, stability, extended drug release, and capacity to deliver poorly soluble medications. The limitations of conventional chemotherapy are overcome in a variety of applications for improved cancer care by metal-based nanoparticles, either used alone or in combination. The dynamic nature of nanotechnology drives continued developments like protein-based nanoparticles and micelles. Polymer and lipid encapsulation within nanocrystals is becoming more and more popular, suggesting a long-term trend. This advancement marks a significant breakthrough in life-saving nanotechnology, particularly in cancer treatment, and sets the stage for pioneering applications in nanomedicine.

Background: Magnesium and potassium are two critical minerals that have been linked to the treatment of diabetes and its consequences. A lack of magnesium has been linked to insulin resistance and diabetes, whereas potassium has been found to promote insulin sensitivity and glucose metabolism. The study aimed to determine the relationship between cholesterol, liver and kidney markers, and quality of life in diabetic patients before and after magnesium and potassium supplementation. Methods: It was a single-blind randomized controlled study at Lahore Garrison University and Lahore Medical Research Centre (LMRC). The study included 200 diabetes participants. Four groups were made based on supplements. Blood samples of all diabetes patients were obtained to assess their quality of life before and after using Mg + and K + supplements, as well as the association between cholesterol, liver, and kidney markers. Results: The participants' average age was 51.0 ± 11.08. 139 (69.5 %) of the 200 participants were female, whereas 26 (30.5 %) were male. There was no correlation between the quality of life measure and the patients' cholesterol levels before and after the magnesium and potassium supplementation. Furthermore, the kidney and liver indicators were not dependent on the diabetes individuals' cholesterol levels. Conclusions: The study concluded that none of the four groups noticed a significant effect of magnesium and potassium therapies on the patient's quality of life or cholesterol levels. However, more research is needed to determine if liver and kidney problems are linked to cholesterol levels before and after medication, as the current study found no significant correlation between the two parameters.

Purpose (i) To evaluate the efficacy of spinach in dry powdered and liquid form as a sustainable substitute for chemical fertilizers; (ii) To analyze and compare the morphological and yield-related parameters of Solanum melongena plants using spinach powder and compost as compared to the control treatment.Method In a pot experiment, Solanum melongena seeds were sowed in soil mixed with spinach powder and compost while the control pots were without any amendment to the soil. The spinach -amended soil, compost-amended soil, and control soil sample were compared in terms physiochemical characteristics like pH , EC ,TDS, organic matter , nutrients i.e. nitrogen , phosphorus, potassium ,calcium ,magnesium and heavy metals. While, plant morphological traits plant height, root length, numbers of flowers and fruits were recorded.Results The pH of spinach-amended soil was slightly alkaline before plantation as compared to control soil. The nitrogen, phosphorus, and potassium content in spinach dry powder was 15%, 36% and 49%, respectively. The organic carbon content was 56% with moderate amount of micronutrients like calcium, magnesium and iron. Spinach powder-amended soil significantly improved the plant morphological characteristics like stem height, root length and able to produce more flowers and mature fruits as compared to compost-amended soil and control soil.Conclusion The use of spinach in powder and liquid concoction was a suitable alternative to chemical fertilizers. It was able to deliver all major macro- and micronutrients to soil thus improving the fertility and has significant impact on plant growth and yield.

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC.

Around 118.5 million blood donations are collected annually to save precious lives. The donated blood may also be associated with blood-borne infections. With around 247 million population, Pakistan is an endemic country for viral hepatitis, and there is a high risk of having asymptomatic blood donors among healthy donors. Viral hepatitis is 2.5% prevalent in the general population, and blood donation and its screening have become grave health concerns for Pakistani health authorities. Asymptomatic viral hepatitis needs screening to rule out subliminally diseased individuals, as recommended by the World Health Organization. Knowing the prevalence of the transfusion transmissible infectious (TTIs) agents in healthy blood donors helps assess the disease burden in any population, boosts treatment rates, and precludes dreaded complications in the affected people. The objective of the current study was to determine the prevalence and trends of significant TTIs among blood donors visiting the Armed Forces Institute of Transfusion (AFIT), Rawalpindi, Pakistan. A total of 15,405 blood donors were screened for HBV, HCV, HIV, malaria, and syphilis during this cross-sectional descriptive study. Most donors had an O-positive blood group; AB-negative donors were only 0.7%. Out of the study population, we reported 1.06% HBV, 0.54% HCV, 0.19% HIV, and 0.31% syphilis-positive asymptomatic blood donors. However, no blood donor was found positive for malaria. The Punjab province was reported as the most burdened for TTIs, and youngsters aged 18–27 years were mainly positive, indicating the need to conduct national-level awareness campaigns about TTIs. The stakeholders need to strengthen the blood collection guidelines, and effective performance should be strictly monitored through internal and external audits considering the aim of reaching non-infectious blood products.

Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for predictions is unknown. Consequently, the reliability of the model becomes questionable in many circumstances. Explainable AI (XAI) plays an important role in improving the transparency and interpretability of the model thereby making it more reliable for real-time deployment. To investigate the reliability and truthfulness of DL models, this research develops image classification models using transfer learning mechanism and validates the results using XAI technique. Thus, the contribution of this research is twofold, we employ three pre-trained models VGG16, MobileNetV2 and ResNet50 using multiple transfer learning techniques for a fruit classification task consisting of 131 classes. Next, we inspect the reliability of models, based on these pre-trained networks, by utilizing Local Interpretable Model-Agnostic Explanations, the LIME, a popular XAI technique that generates explanations for the predictions. Experimental results reveal that transfer learning provides optimized results of around 98% accuracy. The classification of the models is validated on different instances using LIME and it was observed that each model predictions are interpretable and understandable as they are based on pertinent image features that are relevant to particular classes. We believe that this research gives an insight for determining how an interpretation can be drawn from a complex AI model such that its accountability and trustworthiness can be increased.

Primary Amoebic Meningoencephalitis (PAM), a severe lethal brain disease, is caused by a parasite, Naegleria fowleri, also known as the “brain-eating amoeba”. The chances of a patient’s recovery after being affected by this parasite are very low. Only 5% of people are known to survive this life-threatening infection. Despite the fact that N. fowleri causes a severe, fatal infection, there is no proper treatment available to prevent or cure it. In this context, it is necessary to formulate a potential vaccine that could be able to combat N. fowleri infection. The current study aimed at developing a multi-epitope subunit vaccine against N. fowleri by utilizing immunoinformatics techniques and reverse vaccinology approaches. The T- and B-cell epitopes were predicted by various tools. In order to choose epitopes with the ability to trigger both T- and B-cell-mediated immune responses, the epitopes were put through a screening pipeline including toxicity, antigenicity, cytokine-inductivity, and allergenicity analysis. Three vaccine constructs were designed from the generated epitopes linked with linkers and adjuvants. The modeled vaccines were docked with the immune receptors, where vaccine-1 showed the highest binding affinity. Binding affinity and stability of the docked complex were confirmed through normal mode analysis and molecular dynamic simulations. Immune simulations developed the immune profile, and in silico cloning affirmed the expression probability of the vaccine construct in Escherichia coli (E. coli) strain K12. This study demonstrates an innovative preventative strategy for the brain-eating amoeba by developing a potential vaccine through immunoinformatics and reverse vaccinology approaches. This study has great preventive potential for Primary Amoebic Meningoencephalitis, and further research is required to assess the efficacy of the designed vaccine.

ABSTRACT Aspergillus fumigatus is a respiratory pathogen, which has the potential to cause brooder pneumonia in poultry chicks and aspergillosis in birds and farmers. Frequent and irrational use of antifungals has led to the development of resistance in the fungal species. The present study aimed to evaluate the antifungal activity of essential oils (EOs) from four different spices against multi-drug resistant (MDR) A. fumigatus isolated from 40 poultry feed samples. Twenty fungal isolates were identified to be A. fumigatus based on the macroscopic and microscopic characteristics, which were confirmed by targeting and amplifying the species-specific rodA gene (313 bp). Five (5/20) isolates were found to be MDR by the disk diffusion method. EOs were tested as an alternative strategy for 5 MDR isolates of A. fumigatus. Clove EO provided better activity by 6 log10 reduction for 30-90 min exposure as compared to other EOs, therefore, shortlisted for well-diffusion and minimum inhibitory concentration (MIC) method. Clove EO provided promising results as compared to fluconazole, amphotericin B, ketoconazole, nystatin and voriconazole as its mean ZOI (39.6 ± 6.34) were better than all other commercial antifungals (p< 0.001). Mean MIC was reported to be 0.195 ± 0.11 µl/ml. Safety testing of the EO produced 74-82% cell viability for the range of 0.390 - 0.097 µl/ml as compared to DMSO. Findings of the present study highlight that clove EO can be source of a good therapeutic agent for infections of MDR isolates of A. fumigatus.

Objective: To evaluate the effect of constraint-induced movement therapy along with conventional therapy and conventional therapy alone on upper extremity spasticity and quality of life in children with cerebral palsy. Methodology: Quasi experimental study. The research was carried out at Faisal Hospital and Children Hospital & Institute of Child Health in Faisalabad from April, 2022 to July 2022. After informed consent and complete evaluation, individuals who fulfill the inclusion criteria were considered for the study. A sample size of 46 were allocated within two groups with one receiving constraint induced movement therapy along with conventional therapy and other receiving conventional therapy alone three times a week for 4 weeks. The results were assessed at the first (week 0), second (week 2), and fourth (week 4) weeks following therapy using the modified Ashworth scale and the cerebral palsy quality of life questionnaire. Results: The mean MAS scores (pre-treatment p > 0.05) was significantly at the second and fourth weeks following therapy (post treatment p < 0.05). Also, at the second and fourth post-treatment readings, (pre-treatment p > 0.05) there was a statistically significant improvement in the mean CPQOL scores (post treatment p < 0.05) Conclusion: Both groups were proven efficient in reducing spasticity and demonstrated improvement in quality of life, however substantial improvement in results was reported in CIMT group. Keywords: Cerebral Palsy, Spasticity, Health related Quality of life, Constraint Induced Movement Therapy, Conventional therapy.

ABSTRACT Introduction: Abnormal Implantation of the Placenta in uterine walls is the condition known as Morbidly Adherent Placenta. It includes Placenta accreta, increta and percreta. It is mostly reported after history of placental previa and C-section. The irreversible damages from Abnormal Placentation that includes morbidity and mortality in women can be prevented by a holistic approach of treatment. Objective: To determine the frequency of hysterectomy in morbidly adherent placenta in post-cesarean section patients. Methodology: A Hospital based descriptive cross-sectional study of 8 months, from September 2019 to April 2020 was carried out at the Obstetrics and Gynecology department. A total of 210 participants were selected for the study. Non probability consecutive sampling technique was used for data collection. Results: The Frequency of Hysterectomy in morbidly adherent placenta in post Cesarean section women was reported 166 (79.04%), out of the remaining 44 (20.96%) patients the Triple P was reported in 21 (10%) patients and B-Lynch done in 15 (7.14%) patients and 8 (3.8%) were reported with Balloon Tamponade. Conclusion: The frequency of hysterectomy in MAP is high. Attention is required to be given to conservative methods to protect fertility.

About 1.5 million women are diagnosed with breast cancer every year, making it the most frequent disease among women. In Pakistan, one woman in every nine has a lifetime chance of being diagnosed with breast cancer, making it the country with the highest incidence rate of breast cancer in Asia. The mortality rate from breast cancer in Pakistan was 22.7% in 2020. A lack of resources, such as competent pathologists, causes a delay in diagnosis and inadequate therapy planning, all of which contribute to a dismal survival rate. End-to-end solutions that may be implemented into computer-aided diagnostic (CAD) systems have been developed by medical professionals and researchers using domain-specific artificial intelligence (AI) technologies, most notably deep learning models, to address this critical issue. By increasing the amount of work for pathologists, these AI models may help in breast cancer detection and diagnosis. The goal of this research was to compare and contrast the effectiveness of many recent convolutional neural network (CNN) designs. Five pre-trained and fine-tuned deep CNN architectures, InceptionV3, ResNet152V2, MobileNetV2, VGG-16, and DenseNet-121, are tested to determine the best-performing model. The goal is to discover which models are preferable in terms of accuracy and effectiveness. Notably, the pre-trained InceptionV3 model outperforms the basic CNN model by 9%, with a high accuracy level of 94%. ResNet152V2 got 95% accuracy, and MobileNetV2 got 97% accuracy. The VGG-16 model outperforms the competition with a remarkable 98% accuracy rate. Following suit, the DenseNet-121 model achieves a remarkable 99% accuracy. These findings highlight the utility of deep learning models in the diagnosis of breast cancer as well as the range of model precision.

A brain tumor considered the deadliest disease in the world. Patients with misdiagnoses and insufficient treatment have a lower chance of surviving for life. However, for diagnosing the disturbance in the brain, magnetic resonance images play a vital role, but due to the large number of images produced by MRI, it is time-consuming and difficult to diagnose a patient at an earlier stage with high accuracy. Brain tumor detection is challenging due to variations in its size, location, and similarity in health tissues. Further, early detection of brain tumors plays a vital role in enabling a wider range of treatment options, improvements in survival rate, and patients quality of life with reduced healthcare costs. That’s why there is a need for an automatic, accurate system that can detect brain tumor at an earlier stage with an accurate result. So, in this research article, pretrained modals such as AlexNet, VGG16, VGG19, ResNet50, InceptionV3, DenseNet121, and all variants of the EfficientNet model were chosen because of their exceptional performance in tasks like feature extraction and image classification with the capability of detecting anomalies in images. The experiment was done on a publicly available multiclass dataset for brain tumor. Further, before passing the dataset to the model, preprocessing was applied to the dataset in terms of resizing the images to the same size; further images were cropped due to the extra boundaries around the images; noise was removed from the images using the FastNIMeans Denoising colored filter; and a data augmentation technique was applied to reduce the overfitting problem and ensure accurate fast training. On different pretrained deep learning models, the EfficientNetB7 model produced better results in terms of validation accuracy. Further pretrained EfficientNetB7 model were customized by adding a few layers and fine-tuning parameters. Our customized pretrained EfficientNetB7 (CPEB7) model was evaluated in terms of accuracy, loss, precision, sensitivity, specificity, recall, f1-score, and MIOU (mean intersection over union) and the model achieved accuracy of 98.57%, 98.97%, 98.97%, and 99.38% for no, meningioma, pituitary, and glioma tumor classes, respectively. Further, our proposed model achieved an overall accuracy of 98.97% with a 1.02% miss classification rate and MIOU of 95.73%. Further, the proposed model was also evaluated on k-fold cross-validation and achieved 99.097% accuracy on fold-5. This proposed system shows superiority over already existing methods in terms of performance evaluation parameters.

The complex q-Rung orthopair fuzzy (Cq-ROF) sets are an extended version of the fuzzy sets and q-rung orthopair fuzzy circumstances. The Cq-ROF set carried extensive information about an object with two components: membership grade (MG) and non-membership grade (NMG). The MG and NMG enlarged the unit circle to a complex plane. The decision maker evaluates complex and complicated information more accurately considering the Cq-ROF domain. This article aims to present the importance of energy sources, and we discuss the application for the selection of a suitable company selection for dam construction. Dams are the most appropriate and cheap sources to produce electricity. Now, the day’s importance of electricity is not ignorable. So, construct the multi-attribute group decision-making (MAGDM) methodology by utilizing Aczel-Alsina (AA) t-norm (TN) and t-conorm (TCN) operational laws with power aggregation operators (PAO) to solve the appropriate company selection problem for dam construction. PAO provides more accurate aggregation results between the several attributes. Finally, we define the Cq-ROF Aczel-Alsina power-weighted averaging (Cq-ROFAAPWA) and aggregation Cq-ROF Aczel-Alsina power-weighted geometric (Cq-ROFAAPWG) operators. Also, some fundamental axioms are discussed. By applying the proposed Cq-ROFAAPWA and Cq-ROFAAPWG operators, solve the real-life MAGDM problematic issue through a numerical example. For the superiority and applicability of proposed techniques, provide a comprehensive comparative analysis with other prevailing aggregation operators (AOs). In the end, provide solid conclusions.

To reduce the risk and interruptions of the supply chain, some appropriate aggregation operators are used to deal with crucial challenges and to keep the support system reliable. In this article, we explore the notion of the complex picture fuzzy (CPF) set (CPFS), a wider framework of fuzzy and picture fuzzy sets. Some robust operations of the Aczel Alsina aggregation tools are characterized based on CPF information to overcome the impact of uncertain and imprecision situations. We also explain an innovative concept of the prioritized aggregation operators (AOs) in the light of CPF information. The theory of unknown weight vectors is used to develop some new approaches of the CPF environments, including CPF Aczel Alsina prioritized average (CPFAPA) and CPF Aczel Alsina priortized geometric (CPFAPG) operators. We also propose a class of new methodologies under considering CPF information, such as CPF Aczel Alsina prioritized weighted average (CPFAPWA) and CPF Aczel Alsina prioritized weighted geometric (CPFAPWG) operators. To illustrate the flexibility of our invented methodologies, some appropriate characteristics of the proposed approaches are also demonstrated. The multi-attribute decision-making (MADM) technique is used to evaluate many crucial real-life challenges. An algorithm for the MADM problem is also proposed to show the robustness of our invented approaches. By using proposed methodologies, we demonstrate an experimental case study to choose a sustainable supplier agnet based on CPF information. To show the practicability and consistency of our invented approaches by contrasting the results of existing approaches in the literature review with currently presented methodologies.

Lung cancer is the most common cause of cancer deaths worldwide. Early detection is crucial for successful treatment and increasing patient survival rates. Artificial intelligence techniques can play a significant role in the early detection of lung cancer. Various methods based on machine learning and deep learning approaches are used to detect lung cancer. This research works aims to develop automated methods to accurately identify and classify lung cancer in CT scans by using computational intelligence techniques. The process typically involves lobe segmentation, extracting candidate nodules, and classifying nodules as either cancer or non-cancer. The proposed lung cancer classification uses modified U-Net based lobe segmentation and nodule detection model consisting of three phases. The first phase segments lobe using CT slice and predicted mask using modified U-Net architecture and the second phase extracts candidate nodule using predicted mask and label employing modified U-Net architecture. Finally, the third phase is based on modified AlexNet, and a support vector machine is applied to classify candidate nodules into cancer and non-cancer. The experimental results of the proposed methodology for lobe segmentation, candidate nodule extraction, and classification of lung cancer have shown promising results on the publicly available LUAN16 dataset. The modified AlexNet-SVM classification model achieves 97.98% of accuracy, 98.84% of sensitivity, 97.47% of specificity, 97.53% of precision, and 97.70% of F1 for the classification of lung cancer.

Clostridium perfringens toxinotype B is causative agent of lamb dysentery in Pakistan. For assessment of gene expression, it is preferred to optimize toxin production potential of indigenous isolates. Current study aimed to confirm the local isolates of C. perfringens toxinotype B on molecular basis followed by optimization of major toxins under varied physical and chemical conditions. The isolates were identified using microbiological and biochemical assays and confirmed by PCR followed by nucleotide sequencing of 16S rRNA amplified gene. These nucleotide sequences were submitted to NCBI GenBank® and accession numbers retrieved were MW867097, MW867098, MW867099, MW867100 and MW867101. Confirmation of toxinotype B was done by PCR amplification of alpha beta and epsilon toxin genes. Major toxins were optimized at varied physical (temperature and pH) and chemical (reinforced clostridial media, Robertson"s cooked meat media, egg meat media, fluid thioglycolate media and iron milk media) conditions. Higher hemolytic units of alpha toxin (21.45±0.53 HU/mL), epsilon toxin (16.57±0.19 HU/mL) and higher cytotoxic units of beta toxin (18.65±0.34 HU/mL) were produced at 37°C with pre-adjusted pH 6.0 in Robertson cooked meat media. The ELISA percentages of alpha (14.27%, 13.92%, 13.67%, 13.56%, 13.45%), beta (12.43%, 12.81%, 12.39%, 12.61% and 13.07%) and epsilon (13.93%, 14.78%, 14.28%, 14.03% and 13.25%) toxins were also higher at same conditions. These optimized conditions can be used for major toxin gene expression studies of C. perfringens toxinotype B.

Introduction: Toxoplasmosis is a well-known zoonotic disease caused by Toxoplasma gondii. The main causes of the disease range from eating undercooked or contaminated meat and shellfish to cleaning litter trays into which cats that excreted toxoplasma via faeces. This pathogen can live for a very long time, possibly a lifetime, within the bodies of humans and other animals. Aims and objectives: This study aimed to predict and analyse candidate immunogenic epitopes for vaccine development by evaluating the physio-chemical properties, multiple sequence alignment, secondary and tertiary structures, phosphorylation sites, transmembrane domains, and signal peptides, of T. gondii rhoptry proteins ROP7, ROP21, and ROP22 using bioinformatics tools. Methods: To find immunogenic epitopes of rhoptry proteins, numerous bioinformatics web servers were used containing multiple sequence alignment, physiochemical properties, antigenicity and allergenicity, post-translational modification sites (PTMs), signal peptides, transmembrane domains, secondary and tertiary structures, and screening of predicted epitopes. We evaluated immunogenic linear B-cell epitopes as candidate proteins for vaccine development. Results: Nine epitopes were identified for each protein, and analysis of immunogenicity, revealed three candidate epitopes for ROP7, one for ROP21, and four for ROP22. Among all candidate epitopes, ROP22 contained the most immunogenic epitopes with immunogenicity score of 0.50575. Conclusion: We acquired detailed information on predicted immunogenic epitopes using in-silico methods. The results provide a foundation for further experimental analysis of toxoplasmosis, and potential vaccine development.

Background: In cardiac anatomy, the term "dominance" refers to the supply of the posterior descending artery (PDA). Therefore, the PDA might arise from the left circumflex artery (LCX), the right coronary artery (RCA), or both, resulting in left dominant (LD), right dominant (RD), or co-dominant (CD) anatomy, respectively. Few studies have examined the relationship between coronary dominance and coronary artery disease (CAD) severity. CAD severity is defined as single, double, or triple vessel disease based on degree of stenosis. Our study intends to identify coronary dominance trends in Pakistan and show a correlation between coronary dominance and the severity of CAD. Methods: Between Jun 17, 2018 and August 4, 2018 data from coronary angiographies of 631 patients at a tertiary care hospital in Pakistan was collected. Patients were classified as LD, RD, or CD as reported in the results of coronary angiograms. We utilized a chi-square and multinomial logistic regression analyses to assess whether a correlation exists between coronary dominance and CAD severity. Results: Subjects were 78.9% RD, 10.5% LD, and 10.6% CD. A significant relation between dominance and severity of CAD was noted, ꭕ2(8, N=631) = 17.58, p=0.025. Individuals with right dominance had a greater chance of developing triple-vessel disease than single-vessel (p =0.025; OR = 0.451; 95% CI for OR: 0.224–0.906) and two-vessel disease (p = 0.029; OR = 0.471; 95% CI for OR: 0.239–0.926). Conclusion: In our study, right dominance has a positive correlation with severity of coronary artery disease.

This study aims to explore the intersection of Islamic laws and legal frameworks in Pakistan related to environmental protection and ecological sustainability. Numerous Laws and governmental and non-government organizations to protect and sustain the environment have evolved with the passage of time in Pakistan. Islam as a comprehensive religion underpins the Islamic law of ecological sustainability, which stresses humankind's duty to safeguard the ecosystem. Islam's extensive character means its environmental rulings simultaneously apply to individuals, societies, and states. The present investigation intends to offer some insight into the possibility of harmonization and the incorporation of both structures for efficient ecological conservation by studying the ideas, notions, and legal processes existent in Islamic law and Pakistani environmental law. The methodology used in this study is a thorough assessment and comparative analysis of the pertinent literature, which includes scholarly papers, case laws, environmental laws of Pakistan, and Islamic legal texts (Quran, Hadith, etc.). Contemporary ecological paradigms might benefit from the legal and pragmatic suggestions of the Islamic concept of sustainable development. This abstract claims that Islamic law's ideas and precepts can promote equitable growth, ecological awareness, and harmony among humans and nature.

Introduction: Attempted tracheal replacement efforts thus far have had very little success. Major limiting factors have been the inability to efficiently re-vascularise and mimic the mechanical properties of native tissue. The major objective of this study was to optimise a previously developed collagen-hyaluronic acid scaffold (CHyA-B), which has shown to facilitate the growth of respiratory cells in distinct regions, as a potential tracheal replacement device.Methods: A biodegradable thermoplastic polymer was 3D-printed into different designs and underwent multi-modal mechanical assessment. The 3D-printed constructs were incorporated into the CHyA-B scaffolds and subjected to in vitro and ex vivo vascularisation.Results: The polymeric backbone provided sufficient strength to the CHyA-B scaffold, with yield loads of 1.31–5.17 N/mm and flexural moduli of 0.13–0.26 MPa. Angiogenic growth factor release (VEGF and bFGF) and angiogenic gene upregulation (KDR, TEK-2 and ANG-1) was detected in composite scaffolds and remained sustainable up to 14 days. Confocal microscopy and histological sectioning confirmed the presence of infiltrating blood vessel throughout composite scaffolds both in vitro and ex vivo.Discussion: By addressing both the mechanical and physiological requirements of tracheal scaffolds, this work has begun to pave the way for a new therapeutic option for large tracheal defects.

Over the past 20 years, outbreaks related to coronavirus-associated diseases, such as MERS and SARS, have been threatening the whole world. The novel coronavirus emerged in Wuhan, China and belongs to the SARS family. It has been named “Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2)”. Keeping in view the history of pandemics faced by the world, it would be fair to say that each of them has been one of its kind, bringing an equal amount of distress and damage to the humanity. With the help of other countries, Pakistan has coped well with the pandemic. Measures taken by different countries included curfews imposed in Italy, Spain, Russia, and India, while the UK, Ireland, and China opted for a more passive approach. South Korea imposed strict self-isolation requirements across the country, whereas UAE suspended all ferry services from Iran. Vaccines authorized by FDA to treat COVID-19 include Pfizer manufactured by Biotech which has 95% efficiency, Moderna with 94% efficiency, and Johnson and Johnson which has an overall efficiency of 72% and 86% efficiency in case of severe infection. These stats are from the USA. Whereas, vaccines such as Sinopharm, Sinovac, CanSino-Bio, and Sputnik have been administered in Pakistan following their approval by the Drug Regulatory Authority of Pakistan (DRAP). This study aims to review the various aspects of the COVID-19 pandemic such as disease symptoms, the mode of action, a brief comparison of control measures taken by different countries, therapeutic trials to cure COVID-19, and the status of vaccines.

This study investigated the relationship between alexithymia and depression amongst university students and how it is mediated by anxiety. Data was collected from a sample of university students (n= 74, Men = 41, Women = 33) aged 18 to 25 (Mean = 22.05, SD = .38). The findings revealed a statistically significant gender difference with men being more alexithymic than women. There was a significant positive correlation between the alexithymic subscales difficulty identifying feelings (DDF) and difficulty describing feelings (DIF), depression and anxiety. DDF and DIF along with anxiety predicted depression. Anxiety partially mediated the relationship between DIF and depression but completely mediated the relationship between DDF and depression. Alexithymic features DDF and DIF predict depression in the sample but Externally Oriented Thinking (EOT) did not. Anxiety was found to be an underlying mechanism operating in the between alexithymic features and depression.

Abstract Background COVID-19 poses a global health challenge with more than 325 million cumulative cases and above 5 million cumulative deaths reported till January 17, 2022, by the World Health Organization. Several potential treatments to treat COVID-19 are under clinical trials including antivirals, steroids, immunomodulators, non-specific IVIG, monoclonal antibodies, and passive immunization through convalescent plasma. The need to produce anti-COVID-19 IVIG therapy must be continued, alongside the current treatment modalities, considering the virus is still mutating into variants of concern. In this context, as the present study will exploit pooled diversified convalescent plasma collected from recovered COVID-19 patients, the proposed hyperimmune Anti-COVID-19 intravenous immunoglobulin (C-IVIG) therapy would be able to counter new infectious COVID-19 variants by neutralizing the virus particles. After the successful outcome of the phase I/II clinical trial of C-IVIG, the current study aims to further evaluate the safety and efficacy of single low dose C-IVIG in severe COVID-19 patients for its phase II/III clinical trial. Methods This is a phase II/III, adaptive, multi-center, single-blinded, randomized controlled superiority trial of SARS-CoV-2 specific polyclonal IVIG (C-IVIG). Patients fulfilling the eligibility criteria will be block-randomized using a sealed envelope system to receive either 0.15 g/Kg C-IVIG with standard of care (SOC) or standard of care alone in 2:1 ratio. The patients will be followed-up for 28 days to assess the primary and secondary outcomes. Discussion This is a phase II/III clinical trial evaluating safety and efficacy of hyperimmune anti-COVID-19 intravenous immunoglobulin (C-IVIG) in severe COVID-19 patients. This study will provide clinical evidence to use C-IVIG as one of the first-line therapeutic options for severe COVID-19 patients. Trial registration Registered at clinicaltrial.gov with NCT number NCT04891172 on May 18, 2021.

Background: Stammering is a speech fluency disorder categorized by repetitions, prolongations and blocks that interrupt the normal flow and forward movement of speech. The Fluency shaping techniques including Easy Onset and Pantomiming treatment are commonly used techniques for the management of stuttering. Objective: To compare the effectiveness of Pantomiming and easy onset method for the treatment of blocks in stammering in school aged children. Material & Method: A randomized control trial (NCT04813588) was conducted in hospitals of Rawalpindi and Islamabad. The Convenient sampling technique was used for data collection from National Institute of Rehabilitation Medicine (NIRM). The sample size was n=16, randomly divided in Easy onset (n=8) and Pantomiming (n=8) through lottery method. The schools going children and adolescent between 6-18 years of both genders with mild to moderate stammering with blocking symptoms were included. The severity of stuttering was assessed with real time analysis of speech fluency. The data was analysed at the baseline and after three months of intervention. The non-parametric tests were applied for within and between group analyses. Results: The mean age of the participants was 10.38±5.290 years. A total n=11 were males and n=5 were females. When compared both groups, there was no significant difference between Easy onset and Pantomiming technique {MR=9 vs MR=8, U=28, p=0.535} in the management of stuttering after three months of intervention. Conclusion: The easy onset and pantomiming techniques are equally effective in improving the blocking in stuttering. Keywords: Blocks, easy onset, fluency shaping, pantomiming, speech therapy, stammering, stuttering