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Simple Summary: African swine fever is a deadly disease affecting pigs worldwide, causing major economic losses. The virus strain causing the current outbreak in Europe and Asia, known as genotype II, was first identified in Georgia in 2007. This study aimed to analyze the genetic makeup of a strain found in Thailand to better understand its characteristics and how it compares to other strains in Asia. The viral DNA from an infected pig was analyzed and was found to be almost identical to strains from Georgia and China. The Thai strain had eight unique genetic changes, including small mutations in specific genes and deletions in others. This detailed genetic analysis helps track the spread and evolution of the virus, providing important information for developing strategies to control future outbreaks. By understanding these genetic differences, improved measures to protect pig populations can be implemented, thereby reducing the economic impact on the agricultural industry. African swine fever virus (ASFV) has been responsible for the globally devastating epidemics in wild and domesticated pigs. Of the 24 identified ASFV genotypes, genotype II is the primary cause for the pandemic occurring in Europe and Asia since its emergence in Georgia in 2007. The current study aimed to characterize the full-length genomic pattern of the ASFV strain from Thailand, TH1_22/CR (Accession No. PP915735), which was then compared with genomic diversity across other Asian isolates using Georgia 2007/1 (Accession No. FR682468) as the reference. Viral DNA was isolated from the pig spleen sample following library preparation and paired-end sequencing using the MiSeq Illumina platform. The sequenced TH1_22/CR isolate spanned 189,395 nucleotides encoding 193 open reading frames (ORFs), exhibiting maximum nucleotide similarity (99.99%) with Georgian (Georgia 2007/1) and Chinese (Wuhan 2019-1 and China HLJ) isolates. Based on phylogenetic analysis, the TH1_22/CR isolate (Accession No. PP915735) was characterized as genotype II, serogroup 8, and IGR-II due to the presence of three tandem repeat sequences (TRSs). Genetic variations including SNPs and single and polynucleotide indels were identified in TH1_22/CR in agreement with other Asian isolates. For comprehensive analysis, the genome was divided into four regions (I–IV) based on gene location. Overall, the TH1_22/CR isolate demonstrated eight SNPs and indels in its genome. Two unique SNPs were reported in the coding regions of the TH1_22/CR isolate, out of which, a C-591-T substitution was seen in MGF 360-4L and a C-297-T was found in A238L, and four unique SNPs were reported in non-coding regions (NCRs). Furthermore, a 29 bp deletion was observed in the IGR between MGF 110-13La and MGF 110-13Lb, as well as 52 bp deletion in the ASFV G ACD 00350 gene. This comparative analysis establishes the foundational information for future studies on the diversity and phylogeography of this regionally significant genetic sub-group of ASFV. [ABSTRACT FROM AUTHOR]

Simple Summary: African Swine Fever (ASF) is a viral disease that affects pigs, caused by the African Swine Fever Virus (ASFV). With no available treatment or commercial vaccine, ASF poses a severe threat, with 100% mortality in acute cases. The virus spreads through direct and indirect contact, and current control measures include early detection, isolation, and culling of infected pigs. ASFV has a complex genome with genes and encoded proteins that play a key role in viral replication and survival. This review examines the structure and function of these proteins, highlighting both newly discovered and previously known genes involved in different stages of infection and immune response. It also discusses virulence genes and those with unknown functions, proposing potential future interventions. The findings from this study aim to advance our understanding of ASFV, guiding the development of effective vaccines and control strategies, which are essential for protecting the swine industry and supporting global food security. African Swine Fever (ASF) is a lethal contagious hemorrhagic viral disease affecting the swine population. The causative agent is African Swine Fever Virus (ASFV). There is no treatment or commercial vaccine available at present. This virus poses a significant threat to the global swine industry and economy, with 100% mortality rate in acute cases. ASFV transmission occurs through both direct and indirect contact, with control measures limited to early detection, isolation, and culling of infected pigs. ASFV exhibits a complex genomic structure and encodes for more than 50 structural and 100 non-structural proteins and has 150 to 167 open reading frames (ORFs). While many of the proteins are non-essential for viral replication, they play crucial roles in mediating with the host to ensure longevity and transmission of virus in the host. The dynamic nature of ASFV research necessitates constant updates, with ongoing exploration of various genes and their functions, vaccine development, and other ASF-related domains. This comprehensive review aims to elucidate the structural and functional roles of both newly discovered and previously recorded genes involved in distinct stages of ASFV infection and immunomodulation. Additionally, the review discusses the virulence genes and genes with unknown functions, and proposes future interventions. [ABSTRACT FROM AUTHOR]

In recent years, new plant-based foods and drinks have been developed to meet the growing demand for animal-derived alternatives, particularly dairy products. This study investigates the impact of lactic acid fermentation on the organic acids and sugars in oat and buckwheat beverages developed using Lactobacillus johnsonii K4 and Lacticaseibacillus rhamnosus K3, which are potential probiotics. The fermented samples were analyzed for pH changes, bacterial viability, and the concentration of organic acids and sugars over 15 days. The results indicated significant variations in bacterial colony counts, with L. johnsonii K4 showing the highest initial growth. Over 15 days, pH levels decreased, with the most acidic conditions observed in buckwheat beverages. Notably, fermentation led to a significant increase in acetic acid concentration and a reduction in malic acid levels, particularly in buckwheat samples. These findings highlight the dynamic nature of fermentation in enhancing the nutritional profile and shelf-life of plant-based beverages. [ABSTRACT FROM AUTHOR]

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower–upper decomposition (LUD), lower–upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) declared the monkeypox outbreak a public health emergency in June 2022. In Pakistan, positive cases of monkeypox were reported in April 2023. Healthcare workers (HCWs) are considered as a front-line force to combat such outbreaks. A questionnaire-based cross-sectional study was conducted among 11 public sector educational institutions in Punjab, Pakistan, during May and June 2023 among final year medical, pharmacy, and nursing students concerning their knowledge of monkeypox. This included the signs/symptoms of monkeypox. Healthcare students were chosen as they are the HCWs of tomorrow. A total of 389 healthcare students participated in the study, with a mean age of 23.17 ± 1.72 years, and the majority were female. The mean knowledge score was 17.69 ± 4.55 (95% CI 17.24–18.14) out of a maximum total knowledge score of 26 (each correct answer was given a score of 1). The proportion of students with good, moderate, and poor knowledge was 21.6%, 43.2%, and 35.2%, respectively. Age (p = 0.017), gender (p < 0.001), and education (p < 0.001) had a significant impact on the knowledge score. In the multivariate linear regression model, education was the only significant factor linked to knowledge scores. Overall, the majority of future HCWs had moderate knowledge of monkeypox. Consequently, educational activities are needed to improve monkeypox-related knowledge among future HCWs. Furthermore, emerging infectious diseases should be routinely incorporated into HCW curricula. [ABSTRACT FROM AUTHOR]

One of the WHO's strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver's cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver's cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%. [ABSTRACT FROM AUTHOR]

There are typically lower COVID-19 vaccination rates among developing versus higher-income countries, which is exacerbated by greater vaccine hesitancy. However, despite the increasing evidence of safety, parents are still reluctant to vaccinate their children against COVID-19. This is a concern in countries experiencing successive waves, such as Pakistan. Consequently, the objective of this study was to gain better understanding and practice regarding parents vaccinating their children against COVID-19 in Pakistan. A cross-sectional study was conducted to measure parents' attitudes towards vaccinating their children. In total, 451 parents participated in the study, giving a response rate of 70.4%; 67.4% were female, 43.2% belonged to the 40–49 years age group, and 47.7% had three children, with 73% of parents fully immunized against COVID-19. We found that 84.7% of parents did not consider COVID-19 to be a very serious issue, and 53.9% considered that their children were not at high risk of COVID-19. Overall, only a quarter of the study participants had currently vaccinated their children and 11.8% were willing to vaccinate their children in the near future. Parents who had a better knowledge of COVID-19, secondary or higher education, children who had chronic illness, and those parents whose children had been infected with COVID-19 were more likely to have their children vaccinated. The most common reasons for vaccine hesitancy were "my child is not at high risk of COVID-19" (61%) and "I am afraid to put/inject a foreign object inside my child's body" (52.2%). Overall, vaccine acceptance was low among the parents of the children. Those parents with higher education, chronic illnesses, greater knowledge of COVID-19 and its vaccines, and those whose children had been infected with COVID-19 were significantly (p < 0.001) inclined towards vaccinating their children. Effective campaigns as well as awareness sessions are needed to address misinformation and reduce vaccine hesitancy. [ABSTRACT FROM AUTHOR]

Antibiotics are one of the most frequently dispensed classes of medicines. However, excessive misuse and abuse enhances antimicrobial resistance (AMR). Previous studies in Pakistan have documented extensive dispensing of 'Watch' and 'Reserve' antibiotics, which is a concern. In view of this, there is a need to assess current dispensing patterns following COVID-19 in Pakistan. A cross-sectional study was undertaken, collecting dispensing data from 39 pharmacies and 53 drug stores from November 2022 to February 2023. Outlets were principally in urban areas (60.9%), with pharmacists/pharmacy technicians present in 32.6% of outlets. In total, 11,092 prescriptions were analyzed; 67.1% of patients were supplied at least one antimicrobial, 74.3% antibiotics, 10.2% antifungals and 7.9% anthelmintics. A total of 33.2% of antimicrobials were supplied without a prescription. Common indications for dispensed antibiotics were respiratory (34.3%) and gastrointestinal (16.8%) infections, which can be self-limiting. In addition, 12% of antibiotics were dispensed for the prevention or treatment of COVID-19. The most frequent antibiotics dispensed were ceftriaxone (18.4%) and amoxicillin (15.4%). Overall, 59.2% antibiotics were 'Watch' antibiotics, followed by 'Access' (40.3%) and 'Reserve' (0.5%) antibiotics. Of the total antibiotics dispensed for treating COVID-19, 68.3% were 'Watch' and 31.7% 'Access'. Overall, there appeared to be an appreciable number of antibiotics dispensed during the recent pandemic, including for patients with COVID-19, alongside generally extensive dispensing of 'Watch' antibiotics. This needs to be urgently addressed with appropriate programs among pharmacists/pharmacy technicians to reduce AMR. [ABSTRACT FROM AUTHOR]

Prisoners form a population who are highly vulnerable to COVID-19 due to overcrowding, limited movement, and a poor living environment. Consequently, there is a need to ascertain the status of COVID-19 vaccination and factors associated with hesitancy among prisoners. A cross-sectional questionnaire-based study was undertaken among prisoners at three district jails in Punjab Province, Pakistan. A total of 381 prisoners participated and none of the study participants had received an influenza vaccine this year. In total, 53% received at least one dose of a COVID-19 vaccine, with the majority having two doses. The top three reasons of vaccine acceptance were "fear of contracting SARS-CoV-2 infection" (56.9%), "desire to return to a pre-pandemic routine as soon as possible" (56.4%), and "having no doubts on the safety of COVID-19 vaccines" (39.6%). There was no statistically significant difference (p > 0.05) in any demographic variables between vaccinated and unvaccinated prisoners except for age, which was strongly association with COVID-19 vaccine uptake (χ2(3) = 76.645, p < 0.001, Cramer's V = 0.457). Among the unvaccinated prisoners (N = 179), only 16 subsequently showed willingness to receive a COVID-19 vaccine. The top three reasons for hesitancy were: COVID-19 is not a real problem/disease (60.1%), safety concerns (51.1%), and COVID-19 vaccine is a conspiracy (50.3%). Efforts are needed to address their concerns given this population's risks and high hesitancy rates, especially among younger prisoners. [ABSTRACT FROM AUTHOR]

Rock strength, specifically the uniaxial compressive strength (UCS), is a critical parameter mostly used in the effective and sustainable design of tunnels and other engineering structures. This parameter is determined using direct and indirect methods. The direct methods involve acquiring an NX core sample and using sophisticated laboratory procedures to determine UCS. However, the direct methods are time-consuming, expensive, and can yield uncertain results due to the presence of any flaws or discontinuities in the core sample. Therefore, most researchers prefer indirect methods for predicting rock strength. In this study, UCS was predicted using seven different artificial intelligence techniques: Artificial Neural Networks (ANNs), XG Boost Algorithm, Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), Lasso, and Ridge models. The input variables used for rock strength prediction were moisture content (MC), P-waves, and rebound number (R). Four performance indicators were used to assess the efficacy of the models: coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The results show that the ANN model had the best performance indicators, with values of 0.9995, 0.2634, 0.0694, and 0.1642 for R2, RMSE, MSE, and MAE, respectively. However, the XG Boost algorithm model performance was also excellent and comparable to the ANN model. Therefore, these two models were proposed for predicting UCS effectively. The outcomes of this research provide a theoretical foundation for field professionals in predicting the strength parameters of rock for the effective and sustainable design of engineering structures [ABSTRACT FROM AUTHOR]

Background and objectives: The inappropriate use of antibiotics in hospitals can potentially lead to the development and spread of antibiotic resistance, increased mortality, and high economic burden. The objective of the study was to assess current patterns of antibiotic use in leading hospitals of Pakistan. Moreover, the information collected can support in policy-making and hospital interventions aiming to improve antibiotic prescription and use. Methodology and materials: A point prevalence survey was carried out with data abstracted principally from patient medical records from 14 tertiary care hospitals. Data were collected through the standardized online tool KOBO application for smart phones and laptops. For data analysis, SPSS Software was used. The association of risk factors with antimicrobial use was calculated using inferential statistics. Results: Among the surveyed patients, the prevalence of antibiotic use was 75% on average in the selected hospitals. The most common classes of antibiotics prescribed were third-generation cephalosporin (38.5%). Furthermore, 59% of the patients were prescribed one while 32% of the patients were prescribed two antibiotics. Whereas the most common indication for antibiotic use was surgical prophylaxis (33%). There is no antimicrobial guideline or policy for 61.9% of antimicrobials in the respected hospitals. Conclusions: It was observed in the survey that there is an urgent need to review the excessive use of empiric antimicrobials and surgical prophylaxis. Programs should be initiated to address this issue, which includes developing antibiotic guidelines and formularies especially for empiric use as well as implementing antimicrobial stewardship activities. [ABSTRACT FROM AUTHOR]

COVID-19 vaccine hesitancy continues to be a widespread problem in Pakistan due to various conspiracy beliefs, myths and misconceptions. Since the hemodialysis population is at a higher risk of contracting infections, we sought to investigate the current COVID-19 immunization status and reasons for any vaccine hesitancy among these patients in Pakistan. This cross-sectional study was conducted among maintenance hemodialysis patients at six hospitals in the Punjab Province of Pakistan. Data were collected anonymously using a questionnaire. A total of 399 hemodialysis patients took part in the survey, the majority of them were male (56%) and aged 45–64 years. A calculated 62.4% of the patients reported receiving at least one dose of the COVID-19 vaccine. Of those vaccinated (249), 73.5% had received two doses and 16.9% had received a booster dose. The most common reasons for vaccination were "being aware they were at high risk" (89.6%), "fear of getting infected" (89.2%) and "willingness to fight against COVID-19-pandemic" (83.9%). Of the 150 patients who had not yet been vaccinated, only 10 showed a willingness to take the COVID-19 vaccine. The major reasons for refusal included "COVID-19 is not a real problem" (75%), the "corona vaccine is a conspiracy (72.1%)" and "I don't need the vaccine" (60.7%). Our study revealed that only 62% patients receiving hemodialysis were partially or completely vaccinated against COVID-19. Consequently, there is a need to initiate aggressive approaches to educate this high-risk population in order to address their concerns with vaccine safety and efficacy as well as correct current myths and misconceptions to improve the COVID-19 immunization status in this population. [ABSTRACT FROM AUTHOR]

There are concerns with excessive antibiotic prescribing among patients admitted to hospital with COVID-19, increasing antimicrobial resistance (AMR). Most studies have been conducted in adults with limited data on neonates and children, including in Pakistan. A retrospective study was conducted among four referral/tertiary care hospitals, including the clinical manifestations, laboratory findings, the prevalence of bacterial co-infections or secondary bacterial infections and antibiotics prescribed among neonates and children hospitalized due to COVID-19. Among 1237 neonates and children, 511 were admitted to the COVID-19 wards and 433 were finally included in the study. The majority of admitted children were COVID-19-positive (85.9%) with severe COVID-19 (38.2%), and 37.4% were admitted to the ICU. The prevalence of bacterial co-infections or secondary bacterial infections was 3.7%; however, 85.5% were prescribed antibiotics during their hospital stay (average 1.70 ± 0.98 antibiotics per patient). Further, 54.3% were prescribed two antibiotics via the parenteral route (75.5%) for ≤5 days (57.5), with most being 'Watch' antibiotics (80.4%). Increased antibiotic prescribing was reported among patients requiring mechanical ventilation and high WBCs, CRP, D-dimer and ferritin levels (p < 0.001). Increased COVID-19 severity, length of stay and hospital setting were significantly associated with antibiotic prescribing (p < 0.001). Excessive antibiotic prescribing among hospitalized neonates and children, despite very low bacterial co-infections or secondary bacterial infections, requires urgent attention to reduce AMR. [ABSTRACT FROM AUTHOR]

More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification methods. We propose a novel driving fatigue detection framework concentrating on the development of the preprocessing, feature extraction, and classification stages to improve the classification accuracy of fatigue states. The proposed driving fatigue detection framework measures fatigue using a two-electrode ECG. The resampling method and heart rate variability analysis were used to extract features from the ECG data, and an ensemble learning model was utilized to classify fatigue states. To achieve the best model performance, 40 possible scenarios were applied: a combination of 5 resampling scenarios, 2 feature extraction scenarios, and 4 classification model scenarios. It was discovered that the combination of a resampling method with a window duration of 300 s and an overlap of 270 s, 54 extracted features, and AdaBoost yielded an optimum accuracy of 98.82% for the training dataset and 81.82% for the testing dataset. Furthermore, the preprocessing resampling method had the greatest impact on the model's performance; it is a new approach presented in this study. [ABSTRACT FROM AUTHOR]

Reusing organic wastes in land applications would enhance the recovery of resources, following the concepts of the circular economy. The sugarcane-based sugar industry produces various by-products (e.g., sugarcane filter mud, molasses, and bagasse) that have the potential to contribute to crop production and soil fertility, owing to their high contents of organic matter and nutrients. Although the agricultural benefits of compost utilization in agriculture have been well-documented, to the best of our knowledge, few scientific data are currently available on the effects of sugarcane filter mud combined with the application of compost for increasing crop production. Thus, a field experiment was carried out to study how sugarcane filter mud, in combination with two compost sources, affected the growth and yield of sweet maize (Zea mays var. saccharata). We compared (i) two types of compost made from brassica residue and household waste applied at a rate of 9 t ha−1, and (ii) two application rates of sugarcane filter mud: 0 and 2 t ha−1 to two controls without any compost application: one with (+SFM) and one without (−SFM) sugar filter mud. The results highlighted that all crop growth and yield parameters benefited more from the domestic waste compost than from the brassica straw compost. Moreover, the addition of sugar filter mud to the compost further boosted the crop performance. Based on the above results, we concluded that the addition of sugarcane filter mud to locally available composts is a feasible approach for more sustainable production of sweet maize, combining efficient waste disposal and the provision of organic matter to the soil. [ABSTRACT FROM AUTHOR]

Since the emergence of COVID-19, several different medicines including antimicrobials have been administered to patients to treat COVID-19. This is despite limited evidence of the effectiveness of many of these, fueled by misinformation. These utilization patterns have resulted in concerns for patients' safety and a rise in antimicrobial resistance (AMR). Healthcare workers (HCWs) were required to serve in high-risk areas throughout the pandemic. Consequently, they may be inclined towards self-medication. However, they have a responsibility to ensure any medicines recommended or prescribed for the management of patients with COVID-19 are evidence-based. However, this is not always the case. A descriptive cross-sectional study was conducted among HCWs in six districts of the Punjab to assess their knowledge, attitude and practices of self-medication during the ongoing pandemic. This included HCWs working a range of public sector hospitals in the Punjab Province. A total of 1173 HCWs were included in the final analysis. The majority of HCWs possessed good knowledge regarding self-medication and good attitudes. However, 60% were practicing self-medication amid the COVID-19 pandemic. The most frequent medicines consumed by the HCWs under self-medication were antipyretics (100%), antibiotics (80.4%) and vitamins (59.9%). Azithromycin was the most commonly purchase antibiotic (35.1%). In conclusion, HCWs possess good knowledge of, and attitude regarding, medicines they purchased. However, there are concerns that high rates of purchasing antibiotics, especially "Watch" antibiotics, for self-medication may enhance AMR. This needs addressing. [ABSTRACT FROM AUTHOR]

Different methodologies for manipulators have been proposed and applied to robot obstacle detection and avoidance in unstructured environments. These methods include different real-time sensors, observer-based algorithms, and path planning using genetic algorithms. However, sensor design integration is complex and considerably expensive. Moreover, the observer algorithm requires complete system dynamics information, which is difficult to derive. In this regard, genetic algorithms are typically considered slow and difficult to optimize. Accordingly, this study proposes a sensor-less obstacle detection technique using a nonlinear observer (known as sliding perturbation observer (SPO)). Obstacle avoidance is also implemented using a motion planner (known as artificial potential field (APF)). The SPO is a nonlinear observer that only requires the partial position and provides all other states (such as position, velocity) and perturbation (non-linearities and external disturbance). The SPO estimates the external torque at each joint resulting from contact (i.e., collision) with an obstacle. Obstacles are detected and avoided by integrating the SPO and APF. The estimated external torque detects the obstacle location and a repulsive force from the APF is applied to avoid this obstacle. To achieve obstacle avoidance, the sum of all estimated torques must be zero. The proposed technique is applied to a robot manipulator with five degrees of freedom. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) is a leading public health threat, which is exacerbated by the high and inappropriate use of antibiotics. Consequently, there is a need to evaluate knowledge regarding antibiotic use, AMR and the readiness to implement antimicrobial stewardship programs (ASPs) among final year medical, pharmacy and nursing students in Pakistan. This reflects the high and increasing rates of AMR in the country, and students as future healthcare professionals (HCPs). A cross-sectional study was conducted among 1251 final year students from 23 public and private educational institutions in Punjab. The majority of the surveyed participants possessed good knowledge of antibiotic use, AMR and the potential causes of AMR. The most common sources of the information on antibiotics were smartphones (69.9%), peers (35.9%) and medical textbooks (30.6%). However, most surveyed participants were not fully prepared to participate in ASPs. They knew, though, how to reduce AMR by educating HCPs about appropriate prescribing, implementing ASPs and improving laboratory facilities. There was a significant association between antibiotic knowledge and causes of AMR with sex, family income and student type (p < 0.05). Being a student at a public sector university (OR = 4.809; CI = 3.261–7.094; p < 0.001) and age (OR = 0.524, CI = 0.327–0.842; p < 0.008) were among the key factors impacting students' training on ASPs. Educational curricula must be improved to include more information about appropriate antibiotic use and ASPs, along with sufficient training, workshops and clinical rotations in the final year, to fully equip students by graduation. [ABSTRACT FROM AUTHOR]

Pakistan, a dengue-endemic country, has encountered several outbreaks during the past decade. The current study aimed to explore the serotype and genomic diversity of dengue virus responsible for the 2022 outbreak in Pakistan. From August to October 2022, NS-1 positive blood samples (n = 343) were collected from dengue patients, among which, (85%; n = 293) were positive based on RT-PCR. In terms of gender and age, dengue infection was more prevalent in male patients (63%; n = 184), with more adults (21–30 years; n = 94) being infected. The serotyping results revealed DENV-2 to be the most predominant serotype (62%; n = 183), followed by DENV-1 (37%; n = 109) and DENV-3 (0.32%; n = 1). Moreover, a total of 10 samples (DENV-2; n = 8, DENV-1; n = 2) were subjected to whole-genome sequencing. Among these, four were collected in early 2022, and six were collected between August and October 2022. Phylogenetic analysis of DENV-2 sequenced samples (n = 8) revealed a monophyletic clade of cosmopolitan genotype IVA, which is closely related to sequences from China and Singapore 2018, and DENV-1 samples (n = 2) show genotype III, which is closely related to Pakistan isolates from 2019. We also reported the first whole genome sequence of a coinfection case (DENV1-DENV2) in Pakistan detected through a meta-genome approach. Thus, dengue virus dynamics reported in the current study warrant large-scale genomic surveillance to better respond to future outbreaks. [ABSTRACT FROM AUTHOR]

Simple Summary: The Aedes mosquito species are highly adapted to human dwellings where they develop in a large variety of human-made containers, and adults feed on human blood, resulting in the transmission of deadly disease agents. Therefore, the development of attracting and killing tools in the form of traps is thus required In the current studies, our primary focus was on developing low-cost monitoring tools to be exploited for the ultimate suppression & control of dengue vector mosquitoes. The attractive baited traps have shown promising results in laboratory settings and have the potential to significantly reduce the Aedes mosquito populations in the pilot field as well. Monitoring trials in the selected sites were deployed for the population fluctuation of dengue vectors and were compared with the surrounding buffer neighborhood with no traps. Entomological survey data from the study was utilized for recording the reduction in egg-laying mosquitoes in the neighborhood with traps. These traps were designed to reduce the number of mosquitoes through two main actions: (1) Attraction: Aedes species were attracted by the combination of odors emitted by baits in the water inside the trap and the black color of the trap; (2) Adulticide: the inside walls of the trap were lined with insecticide deltamethrin that kills resting adults. Dengue virus, transmitted by Aedes aegypti mosquitoes, is the most important emerging viral disease, infecting more than 50 million people annually. Currently used sticky traps are useful tools for monitoring and controlling Ae. aegypti. Therefore, this study was conducted to evaluate the attraction of Ae. aegypti mosquitoes using various colors, materials and insecticides. The laboratory and field assessed the four different colors of ovitraps (blue, green, black and transparent). Among the tested ovitraps, the black ovitraps showed the highest number of eggs (348.8) in the laboratory and maximum eggs (80.0) in field trials. In addition, six different materials (casein, urea, yeast, fish meal, chicken meal and water) were also used to evaluate mosquito's attraction. In our results, the highest number of eggs were collected with fish meal having 0.5% concentration in both laboratory (195.17) and the field (100.7). In laboratory trials, the Deltamethrin treated ovitraps (treated with Deltamethrin) significantly trapped and killed the highest percent of female Ae. aegypti (91.5%) compared to untreated (not-treated with Deltamethrin) ovitraps (3.3%). In field trials, the lethality was determined by installing 10 lethal ovitraps in one block and 10 untreated ovitraps in another block. The results indicate a significant reduction in eggs collected from the treated block (727 eggs) as compared to the untreated block (1865 eggs). The data also reveal that the ovitrap positive index (50) and egg density index (24.3) were also low in treated areas than in untreated areas, 83.3 and 37.3, respectively. It is concluded that the lethal ovitraps significantly reduced the Ae. aegypti population and thus could be considered an integral part of the integrated vector management (IVM) program. [ABSTRACT FROM AUTHOR]

Healthcare-associated infections (HAIs) have a considerable impact on morbidity, mortality and costs. The COVID-19 pandemic resulted in an appreciable number of hospitalized patients being admitted to intensive care units (ICUs) globally with a greater risk of HAIs. Consequently, there is a need to evaluate predictors and outcomes of HAIs among COVID-19 patients admitted to ICUs. A retrospective study of patients with COVID-19 admitted to ICUs of three tertiary care hospitals in the Punjab province over a five-month period in 2021 was undertaken to ascertain predictors and outcomes of HAIs. Of the 4534 hospitalized COVID-19 patients, 678 were admitted to ICUs, of which 636 patients fulfilled the inclusion criteria. Overall, 67 HAIs were identified among the admitted patients. Ventilator-associated lower respiratory tract infections and catheter-related urinary tract infections were the most frequent HAIs. A significantly higher number of patients who developed HAIs were on anticoagulants (p = 0.003), antithrombotic agents (p < 0.001), antivirals (p < 0.001) and IL-6 inhibiting agents (p < 0.001). Secondary infections were significantly higher in patients who were on invasive mechanical ventilation (p < 0.001), had central venous access (p = 0.023), and urinary catheters (p < 0.001). The mortality rate was significantly higher in those with secondary infections (25.8% vs. 1.2%, p < 0.001). Our study concluded that COVID-19 patients admitted to ICUs have a high prevalence of HAIs associated with greater mortality. Key factors need to be addressed to reduce HAIs. [ABSTRACT FROM AUTHOR]

Introduction: The vaccination of children against Coronavirus disease (COVID-19) is a prime area of focus around the globe and is considered a pivotal challenge during the ongoing pandemic. This study aimed to assess parents′ intentions to vaccinate their children and the barriers related to pediatric COVID-19 vaccination. Methodology: An online web-based survey was conducted to recruit parents with at least one child under the age of 12 years from Saudi Arabia's Al-Jouf region. The parental intentions to vaccinate children were assessed via six items, while barriers against vaccination were assessed through seven items in validated study instrument. A 5-point Likert scale was used to record the responses of parents regarding both their intentions and barriers. Results: In total, 444 parents (28.41 ± 7.4 years, 65% females) participated in this study. Almost 90% of parents were vaccinated against COVID-19 but only 42% of parents intended to vaccinate their children. The mean intention score was 2.9 ± 1.36. More than one-third of study participants had no plan to vaccinate their children against COVID-19. The majority of the respondents agreed to vaccinate their children if vaccination was made compulsory by the government (relative index: 0.76, 73%). Out of seven potential barriers analyzed, concerns over vaccine safety and side effects were ranked highest (RII: 0.754), reported by 290 (65%) participants. In multivariate logistic regression, significant predictors of parental intention to vaccinate children were the increased education level of the parents (secondary education: OR = 3.617, p = 0.010; tertiary education: OR = 2.775, p = 0.042), COVID-19 vaccination status (vaccinated: OR = 7.062, p = 0.003), mother's involvement in decisions regarding the child's healthcare (mother: OR 4.353, p < 0.001; both father and mother: OR 3.195, p < 0.001) and parents' trust in the vaccine's safety (OR = 2.483, p = 0.022). Conclusions: This study underscored the low intention among parents to vaccinate their children against COVID-19. Vaccination intention was found to be associated with education, parents' vaccination status, the mother's involvement in healthcare decisions, and parents' trust in the vaccine's safety. On the other hand, parents' concerns over the safety and efficacy of the COVID-19 vaccine were widely reported as barriers to childhood vaccination. The health authorities should focus on addressing parental concerns about vaccines to improve their COVID-19 vaccination coverage. [ABSTRACT FROM AUTHOR]

The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR89) and Point load index (I50) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R2, MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field. [ABSTRACT FROM AUTHOR]

Vaccination protects people from serious illness and associated complications. Conspiracy theories and misinformation on vaccines have been rampant during the COVID-19 pandemic and are considered significant drivers of vaccine hesitancy. Since vaccine hesitancy can undermine efforts to immunize the population against COVID-19 and interferes with the vaccination rate, this study aimed to ascertain the COVID-19-vaccine-related conspiracy beliefs, vaccine hesitancy, views regarding vaccine mandates, and willingness to pay for vaccines among the general population. A web-based, cross-sectional survey was conducted (April–August 2021) among the adult population in six countries (Pakistan, Saudi Arabia, India, Malaysia, Sudan, and Egypt). Participants were recruited using an exponential, non-discriminate snowball sampling method. A validated self-completed electronic questionnaire was used for the data collection. All the participants responded to questions on various domains of the study instrument, including conspiracy beliefs, vaccine hesitancy, and willingness to pay. The responses were scored according to predefined criteria and stratified into various groups. All data were entered and analyzed using SPSS version 22. A total of 2481 responses were included in the study (Pakistan 24.1%, Saudi Arabia 19.5%, India 11.6%, Malaysia 8.1%, Sudan 19.3%, and Egypt 17.3%). There was a preponderance of participants ≤40 years old (18–25 years: 55.8%, 26–40 years: 28.5%) and females (57.1%). The average score of the COVID-19 vaccine conspiracy belief scale (C19V-CBS) was 2.30 ± 2.12 (median 2; range 0–7). Our analysis showed that 30% of the respondents were found to achieve the ideal score of zero, indicating no conspiracy belief. The mean score of the COVID-19 vaccine hesitancy scale (C19V-HS) was 25.93 ± 8.11 (range: 10–50). The majority (45.7%) had C19V-HA scores of 21–30 and nearly 28% achieved a score greater than 30, indicating a higher degree of hesitancy. There was a significant positive correlation between conspiracy beliefs and vaccine hesitancy (Spearman's rho = 0.547, p < 0.001). Half of the study population were against the vaccine mandate. Respondents in favor of governmental enforcement of COVID-19 vaccines had significantly (p < 0.001) lower scores on the C19V-CBS and C19V-HS scale. Nearly 52% reported that they would only take vaccine if it were free, and only 24% were willing to pay for COVID-19 vaccines. A high prevalence of conspiracy beliefs and vaccine hesitancy was observed in the targeted countries. Our findings highlight the dire need for aggressive measures to counter the conspiracy beliefs and factors underlying this vaccine hesitancy. [ABSTRACT FROM AUTHOR]

This study aimed to assess the vaccination status and factors contributing to vaccine hesitancy among pregnant women in the largest province of Pakistan. A multicentric, prospective, survey-based study using an interviewer-administered tool was conducted among pregnant women attending antenatal clinics between 1 December 2021 through 30 January 2022 across seven hospitals in Pakistan. The healthcare professionals providing care at the participating hospitals administered the survey. Four hundred and five pregnant women fully consented and completed the study. The majority of the study participants (70.6%, n = 286) were aged between 25 and 34 and had a previous successful pregnancy history. More than half of the study participants (56.0%, n = 227) did not receive COVID-19 vaccination at the time of data collection despite their family members (93.9%, n = 372) had already received at least one dose of COVID-19 vaccine. Among those who received COVID-19 vaccination (n = 173), vaccine efficacy, protection for the foetus, and risk of COVID-19-associated hospitalisation were the main driving factors for vaccine hesitancy. The majority of the unvaccinated women (77.8%, n = 182) had no intention of receiving the vaccine. However, more than two-thirds (85.7%, n = 342) consulted the doctor about COVID-19 vaccines, and most were recommended to receive COVID-19 vaccines by the doctors (80.7%, n = 280). Women were significantly more likely to be vaccinated if they had employment (odds ratio [OR] 4.47, 95% confidence interval [CI]: 2.31–8.64) compared with their counterparts who were homemakers, consulted their doctors (OR 0.12, 95% CI: 0.04–0.35), and if they did not have pregnancy-related issues (OR 6.02, 95% CI: 2.36–15.33). In this study, vaccine hesitancy was prevalent, and vaccine uptake was low among pregnant women. Education and employment did impact COVID vaccination uptake, emphasising the need for more targeted efforts to enhance the trust in vaccines. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy is widespread in many parts of the globe, particularly in low–middle-income countries. Therefore, we surveyed a sample of hospitalized COVID-19 patients to assess COVID-19 vaccine acceptance and vaccine hesitancy in a low–middle-income country. A cross-sectional sample of 385 confirmed reverse transcriptase–polymerase chain reaction COVID-19 patients treated at secondary and tertiary care hospitals in Punjab, Pakistan, were analyzed to assess COVID-19 vaccine uptake and vaccine hesitancy. The construct validity and reliability of the 11-item vaccine hesitancy questionnaire were also examined. In addition, multivariate logistic regression was used. The majority of the COVID-19 patients admitted to hospitals were not vaccinated (84%). Of those who were willing to receive vaccination, the majority (55%) considered vaccines an effective way to protect people from COVID-19. However, those who were not willing to receive their COVID-19 vaccine had significantly higher hesitancy than those willing to receive their COVID-19 vaccine. In addition, older hospitalized COVID-19 patients aged 60 years or above (20–29 years: OR 0.10; 95% CI 0.01–0.72, p = 0.001) and patients from urban areas (OR 3.16 95% CI 1.27–7.87, p = 0.013) were more likely to receive the COVID-19 vaccine than younger patients and patients from rural areas. Patients with no formal education had significantly higher hesitancy (OR 5.26; 96% CI 1.85–14.97, p = 0.002) than participants with graduation and above education. More than half of the study's participants did not trust information shared on social media about COVID-19 vaccines and cited newspapers/news channels as their main source of information. The study provides important insights into COVID-19 vaccine acceptance and the impact of vaccination campaigns. Many unvaccinated COVID-19 patients in hospitals highlight the need for an effective vaccination drive to protect people from acquiring infection and subsequent hospitalization. [ABSTRACT FROM AUTHOR]

Poly (N-isopropylacrylamide-acrylic acid) [p(NIPAM-AAc)] microgel was successfully fabricated using the precipitation polymerization method. Silver (Ag) nanoparticles and graphene oxide (G) were used to fabricate the following hybrid microgels: Ag-p(NIPAM-AAc) (Ag-HMG), Ag-G-p(NIPAM-AAc) (Ag-G-HMG), and G-p(NIPAM-AAc) (G-HMG). Ag-HMG, Ag-G-HMG, and G-HMG were characterized using a Zetasizer and UV-Vis spectroscopy. The reduction of a series of different compounds with comparable and distinct chemical structures was catalyzed by synthesized Ag-HMG, Ag-G-HMG, and G-HMG hybrid microgels. The average size of Ag nanoparticles was found to be ~50 nm. Ag nanoparticles were synthesized within microgels attached to G sheets. Ag-p(NIPAM-AAc), Ag-G-p(NIPAM-AAc), and G-p(NIPAM-AAc) hybrid microgels were used for the catalytic reduction of nitroarenes and dyes. By comparing their apparent rate constant (kapp), reduction duration, and percentage reduction, the activity of HMG (hybrid microgel) as a catalyst towards different substrates was investigated. Graphene sheets play role in electron relay among Ag nanoparticles and microgels. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DES), which have low toxicity and are low cost, biodegradable, and easily synthesized, were used for the extraction of neutral red (NR) dye before its spectrophotometric analysis. DES, containing choline chloride as a hydrogen bond acceptor and phenol as a hydrogen bond donor with a molar ratio of 1:2, was used for the extraction of NR dye from aqueous media. The possible interaction of different DESs with NR was studied using density functional theory (DFT) calculations. Experimentally, a UV-visible spectrophotometer was used for the quantitative analysis. The most important parameters affecting method performance, such as pH, extraction temperature, DES type, its volume, THF volume, sonication time, and centrifugation time, were optimized. The developed method provides exceptional sensitivity in terms of LOD and LOQ, which were 2.2 and 7.3 µg/L respectively. The relative standard deviation was 1.35–1.5% (n = 10), and the pre-concentration factor was 40. The method was found to be linear in the range of 2–300 µg/L (R2 = 0.9967). The method was successfully used for the determination of NR in wastewater samples. Finally, the DES-based method presents operational simplicity, high sensitivity, and rapid determination (<5 min) compared with other analytical procedures. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) compromises global health due to the associated morbidity, mortality, and costs. The inappropriate use of antimicrobial agents is a prime driver of AMR. Consequently, it is imperative to gain a greater understanding of current utilization patterns especially in high-risk groups including neonates and children. A point prevalence survey (PPS) was conducted among three tertiary care children's hospitals in the Punjab province using the World Health Organization (WHO) methodology. Antibiotic use was documented according to the WHO AWaRe classification. Out of a total of 1576 neonates and children, 1506 were prescribed antibiotics on the day of the survey (prevalence = 95.5%), with an average of 1.9 antibiotics per patient. The majority of antibiotics were prescribed in the medical ward (75%), followed by surgical ward (12.8%). Furthermore, 56% of antibiotics were prescribed prophylactically, with most of the antibiotics (92.3%) administered via the parenteral route. The top three indications for antibiotics were respiratory tract infections (34.8%), gastrointestinal infections (15.8%), and prophylaxis for medical problems (14.3%). The three most common antibiotics prescribed were ceftriaxone (25.8%), amikacin (9.2%), and vancomycin (7.9%). Overall, 76.6% of the prescribed antibiotics were from Watch category followed by 21.6% from the Access group. There was a very high prevalence of antibiotic use among hospitalized neonates and children in this study. Urgent measures are needed to engage all the stakeholders to formulate effective ASPs in Pakistan, especially surrounding Watch antibiotics. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) is a leading global health threat, increasing morbidity, mortality, and costs, with excessive and irrational use of antimicrobials contributing to the development of AMR. Consequently, the aims of this study were to evaluate the understanding of antibiotic use, AMR, and antimicrobial stewardship programs (ASPs) among pharmacy technicians serving in ambulatory healthcare settings in Pakistan. A cross-sectional survey was conducted among pharmacy technicians serving in 144 ambulatory care settings in seven districts of Punjab province using a validated questionnaire. Overall, 376 technicians completed the survey (85.8% response rate). The majority were men (89.1%), aged 25–35 years (45.1%), serving in emergency departments (43.9%) and filling 31–60 prescriptions per day (37.5%). Most (79.5%) knew that antibiotics were one of the most frequently prescribed drug classes, while 59.8% believed antibiotics for common colds did not speed up recovery. Inadequate duration (59.6%) and inadequate dosages (57.7%) of antibiotic therapy were reported as the leading causes of AMR. Terms including 'superbugs', 'multidrug resistance', and 'extensively drug resistance' were known to 42.0%, 25.3%, and 20.7% of participants, respectively; however, <10% knew about ASPs, including their core elements and purpose. Our study revealed that pharmacy technicians have adequate awareness of antibiotic use but are currently unaware of AMR and ASPs, which is a concern. [ABSTRACT FROM AUTHOR]

Background: COVID-19 patients are typically prescribed antibiotics empirically despite concerns. There is a need to evaluate antibiotic use among hospitalized COVID-19 patients during successive pandemic waves in Pakistan alongside co-infection rates. Methods: A retrospective review of patient records among five tertiary care hospitals during successive waves was conducted. Data were collected from confirmed COVID-19 patients during the first five waves. Results: 3221 patients were included. The majority were male (51.53%), residents from urban areas (56.35%) and aged >50 years (52.06%). Cough, fever and a sore throat were the clinical symptoms in 20.39%, 12.97% and 9.50% of patients, respectively. A total of 23.62% of COVID-19 patients presented with typically mild disease and 45.48% presented with moderate disease. A high prevalence of antibiotic prescribing (89.69%), averaging 1.66 antibiotics per patient despite there only being 1.14% bacterial co-infections and 3.14% secondary infections, was found. Antibiotic use significantly increased with increasing severity, elevated WBCs and CRP levels, a need for oxygen and admittance to the ICU; however, this decreased significantly after the second wave (p < 0.001). Commonly prescribed antibiotics were piperacillin plus an enzyme inhibitor (20.66%), azithromycin (17.37%) and meropenem (15.45%). Common pathogens were Staphylococcus aureus (24.19%) and Streptococcus pneumoniae (20.96%). The majority of the prescribed antibiotics (93.35%) were from the WHO's "Watch" category. Conclusions: Excessive prescribing of antibiotics is still occurring among COVID-19 patients in Pakistan; however, rates are reducing. Urgent measures are needed for further reductions. [ABSTRACT FROM AUTHOR]

Macrocyclic lactones, particularly the avermectins, have completely revolutionized the approaches aimed at control of parasites. These avermectins are the most widely used anti-parasitic drugs in veterinary field with sales exceeding one billion US dollars annually. However, before clinical usage, their safety evaluation in the animals is a major critical factor that must be considered. Many studies have reported the negative effects of avermectins like ivermectin, abamectin, doramectin, and eprinomectin on the host animals. These harmful effects arise from avermectins targeting GABA and glutamate-gated chloride channels present both in the parasites and the host animals. In this review, various modes of avermectins action along with the negative effects on the host like nephrotoxicity, hepatotoxicity, neurotoxicity, reproductive toxicity, and endocrine disruption were discussed in detail. Furthermore, other important issues like ecotoxicity, drug resistance, and drug residues in milk associated with avermectins usage were also discussed, which need special attention. [ABSTRACT FROM AUTHOR]

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of 'Big Data' because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients. [ABSTRACT FROM AUTHOR]

Snort is a well-known, signature-based network intrusion detection system (NIDS). The Snort sensor must be placed within the same physical network, and the defense centers in the typical NIDS architecture offer limited network coverage, especially for remote networks with a restricted bandwidth and network policy. Additionally, the growing number of sensor instances, followed by a quick increase in log data volume, has caused the present system to face big data challenges. This research paper proposes a novel design for a cloud-based Snort NIDS using containers and implementing big data in the defense center to overcome these problems. Our design consists of Docker as the sensor's platform, Apache Kafka, as the distributed messaging system, and big data technology orchestrated on lambda architecture. We conducted experiments to measure sensor deployment, optimum message delivery from the sensors to the defense center, aggregation speed, and efficiency in the data-processing performance of the defense center. We successfully developed a cloud-based Snort NIDS and found the optimum method for message-delivery from the sensor to the defense center. We also succeeded in developing the dashboard and attack maps to display the attack statistics and visualize the attacks. Our first design is reported to implement the big data architecture, namely, lambda architecture, as the defense center and utilize rapid deployment of Snort NIDS using Docker technology as the network security monitoring platform. [ABSTRACT FROM AUTHOR]

Infectious diseases pose substantial challenges to the healthcare system and are associated with significant morbidity and mortality. It is pertinent to mention that the COVID-19 pandemic also resulted in improved healthcare metrics for other infectious diseases, i.e., improved influenza vaccination rate during the pandemic. However, the COVID-19 pandemic has caused disruptions in reporting of other infectious diseases, such as dengue, sexually transmitted diseases, and other coronavirus infections [[3]]. [Extracted from the article]

Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term-document matrix construction. [ABSTRACT FROM AUTHOR]

Exploring biological agents to control biofilm is a vital alternative in combating pathogenic bacteria that cause dental plaque. This study was focused on antimicrobial, biofilm formation and biofilm dispersal efficacy of Gallic acid (GA) against bacteria, including Proteus spp., Escherichia coli, Pseudomonas spp., Salmonella spp., Streptococcus mutans, and Staphylococcus aureus and multispecies bacteria. Biofilm was qualitatively and quantitatively assessed by crystal violet assay, florescence microscopy (bacterial biomass (µm2), surface coverage (%)) and extracellular polymeric substances (EPS). It was exhibited that GA (1–200 mg/L) can reduce bacterial growth. However, higher concentrations (100–200 mg/L) markedly reduced (86%) bacterial growth and biofilm formation (85.5%), while GA did not exhibit any substantial dispersal effects on pre-formed biofilm. Further, GA (20–200 mg/L) exhibited 93.43% biomass reduction and 88.6% (p < 0.05) EPS (polysaccharide) reduction. Microscopic images were processed with BioImageL software. It was revealed that biomass surface coverage was reduced to 2% at 200 mg/L of GA and that 13,612 (µm2) biomass was present for control, while it was reduced to 894 (µm2) at 200 mg/L of GA. Thus, this data suggest that GA have antimicrobial and biofilm control potential against single and multispecies bacteria causing dental plaque. [ABSTRACT FROM AUTHOR]

With this study, we aim to assess integration possibilities of formal and informal waste management approaches in a low-income country context by using recycling as a tool towards sustainability. Using a holistic approach, this study examines both formal and informal aspects of recycling value chains through a stakeholder lens. Pakistan is far from being a circular economy, as evident from the dismal levels of municipal solid waste collection and recycling. This problem calls for the effective integration of formal and informal recycling business processes to fill the void. With this study, we seek to assess various aspects of formal and informal waste recycling in Pakistan through an inductive qualitative research design by examining the case of one of the most populous cities, Lahore, in the Punjab province of Pakistan. Equal representation to various stakeholders is accorded, with 25 interviews each with managers of private companies, public companies, waste pickers and scrap dealers in the target area. The study highlights that recycling sector in Pakistan is completely informal and unregulated with no or little support available due to a lack of funds and systematic planning. The empirical evidence suggests that the formal sector lacks the capacity for proper waste disposal of total waste generated. The recycling aspects are left to informal sector stakeholders who improvise to extract value from MSW in an entrepreneurial manner. The formal sector holds a bias towards informal stakeholders due to the fact that their work also serves as a theft bargain and resale platform under the guise of waste collection and recycling. The study highlights the most and least sought after recyclables, identifies gray channel markets of spare parts, metal recycling and counterfeit products, and practices of adulteration in the target area. This study recommends as a policy input, to overcome bias towards the informal sector, the creation of a central recycling commission to overlook and regulate the affairs of hitherto unregulated and informal municipal solid waste and recycling value chains. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is mushrooming as a silent pandemic. It is considered among the most common priority areas identified by both national and international agencies. The global development of multidrug-resistant strains now threatens public health care improvement by introducing antibiotics against infectious agents. These strains are the product of both continuous evolution and unchecked antimicrobial usage (AMU). The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are the leading cause of nosocomial infections throughout the world. Most of them are now multidrug-resistant, which pose significant challenges in clinical practice. Understanding these bacteria's resistance mechanisms is crucial for developing novel antimicrobial agents or other alternative tools to fight against these pathogens. A mechanistic understanding of resistance in these pathogens would also help predict underlying or even unknown mechanisms of resistance of other emerging multidrug-resistant pathogens. Research and development to find better antibacterial drugs and research on tools like CRISPER-Cas9, vaccines, and nanoparticles for treatment of infections that can be further explored in the clinical practice health sector have recognized these alternatives as essential and highly effective tools to mitigate antimicrobial resistance. This review summarizes the known antimicrobial resistance mechanisms of ESKAPE pathogens and strategies for overcoming this resistance with an extensive overview of efforts made in this research area. [ABSTRACT FROM AUTHOR]

In this research, silica nano-particles are deposited over the borosilicate glass and silicon wafer substrates by indigenously developed Aerosol impact deposition assembly using SiH4, oxygen, and helium as precursors. The coating process involves deionization of gases leading towards nucleation sites for silica in the presence of plasma, while tuning the pressure difference between reaction and deposition chamber controls the coating thickness, porosity, and refractive index. The deposited coating layer on the substrate enhanced the transmittance to 99.6% at 600 nm wavelength. The induced porous nature and the graded index of the coated layer as observed from the AFM and SEM resulted in superhydrophilic behavior with a water contact angle of near to 0°. The super-hydrophilicity of the coating contains self-cleaning properties, suggesting an improvement of the performance of solar PV modules as well. [ABSTRACT FROM AUTHOR]

COVID-19 is primarily a respiratory disease, but numerous studies have indicated the involvement of various organ systems during the course of illness. We conducted a comprehensive review of atypical complications of COVID-19 with their incidence range (IR) and their impact on hospitalization and mortality rates. We identified 97 studies, including 55 research articles and 42 case studies. We reviewed four major body organ systems for various types of atypical complications: (i) Gastro-intestinal (GI) and hepatobiliary system, e.g., bowel ischemia/infarction (IR: 1.49-83.87%), GI bleeding/hemorrhage (IR: 0.47-10.6%), hepatic ischemia (IR: 1.0-7.4%); (ii) Neurological system, e.g., acute ischemic stroke/cerebral venous sinus thrombosis/cerebral hemorrhage (IR: 0.5-90.9%), anosmia (IR: 4.9-79.6%), dysgeusia (IR: 2.8-83.38%), encephalopathy/encephalitis with or without fever and hypoxia (IR: 0.19-35.2%); (iii) Renal system, e.g., acute kidney injury (AKI)/acute renal failure (IR: 0.5-68.8%); (iv) Cardiovascular system, e.g., acute cardiac injury/non-coronary myocardial injury (IR: 7.2-55.56%), arrhythmia/ventricular tachycardia/ventricular fibrillation (IR: 5.9-16.7%), and coagulopathy/venous thromboembolism (IR: 19-34.4%). This review encourages and informs healthcare practitioners to keenly monitor COVID-19 survivors for these atypical complications in all major organ systems and not only treat the respiratory symptoms of patients. Post-COVID effects should be monitored, and follow-up of patients should be performed on a regular basis to check for long-term complications.

Background: Individuals with underlying chronic illnesses have demonstrated considerable hesitancy towards COVID-19 vaccines. These concerns are primarily attributed to their concerns over the safety profile. Real-world data on the safety profile among COVID-19 vaccinees with comorbid conditions are scarce. This study aimed to ascertain the side-effects profile after two doses of COVID-19 vaccines among chronic-disease patients. Methodology: A cross-sectional questionnaire-based study was conducted among faculty members with comorbid conditions at a public educational institute in Saudi Arabia. A 20-item questionnaire recorded the demographics and side effects after the two doses of COVID-19 vaccines. The frequency of side effects was recorded following each dose of vaccine, and the association of the side-effects score with the demographics was ascertained through appropriate statistics. Results: A total of 204 patients with at least one comorbid condition were included in this study. A total of 24 side effects were reported after the first dose and 22 after second dose of the COVID-19 vaccine. The incidence of at least one side effect was 88.7% and 95.1% after the first and second doses of the vaccine, respectively. The frequent side effects after the first dose were pain at the injection site (63.2%), fatigue (58.8%), fever (47.5%), muscle and joint pain (38.7%), and headache (36.3%). However, pain at the injection site (71.1%), muscle and joint pain (62.7%), headache (49.5%), fever (45.6%), and stress (33.3%) were frequent after the second dose. The average side-effects score was 4.41 ± 4.18 (median: 3, IQR: 1, 6) and 4.79 ± 3.54 (median 4, IQR: 2, 6) after the first and second dose, respectively. Female gender, diabetes mellitus, hypertension, hyperlipidemia, comorbidity > 2, family history of COVID-19, and the AstraZeneca vaccine were significantly associated with higher side-effect scores. Only 35.8% of study participants were satisfied with the safety of COVID-19 vaccines. Conclusions: Our analysis showed a high proportion of transient and short-lived side effects of Pfizer and AstraZeneca vaccines among individuals with chronic illnesses. However, the side-effects profile was comparable with the safety reports of phase 3 clinical trials of these vaccines. The frequency of side effects was found to be associated with certain demographics, necessitating the need for further investigations to establish a causal relationship. The current study’s findings will help instill confidence in the COVID-19 vaccines among people living with chronic conditions, overcome vaccine hesitancy, and increase vaccine coverage in this population.