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AbstractThe most common and contagious bacterial skin disease i.e. skin sores (impetigo) mostly affects newborns and young children. On the face, particularly around the mouth and nose area, as well as on the hands and feet, it typically manifests as reddish sores. In this study, a neuro-evolutionary global algorithm is introduced to solve the dynamics of nonlinear skin sores disease model (SSDM) with the help of an artificial neural network. The global genetic algorithm is integrated with local sequential quadratic programming (GA-LSQP) to obtain the optimal solution for the proposed model. The designed differential model of skin sores disease is comprised of susceptible (S), infected (I), and recovered (R) categories. An activation function based neural network modeling is exploited for skin sores system through mean square error to achieve best trained weights. The integrated approach is validated and verified through the comparison of results of reference Adam strategy with absolute error analysis. The absolute error results give accuracy of around 10−11 to 10−5, demonstrating the worthiness and efficacy of proposed algorithm. Additionally, statistical investigations in form of mean absolute deviation, root mean square error, and Theil’s inequality coefficient are exhibited to prove the consistency, stability, and convergence criteria of the integrated technique. The accuracy of the proposed solver has been examined from the smaller values of minimum, median, maximum, mean, semi-interquartile range, and standard deviation, which lie around 10−12 to 10−2. [ABSTRACT FROM AUTHOR]

This investigation aims to investigate the pine wilt disease model (PWDM) employing hybrid bio-inspired algorithm. The artificial neural networks-based genetic algorithm (ANNs-GA) as global search and sequential quadratic programming (SQP) serve as local search framework. The model consists of two populations, i.e. host (h) and vector (v). There are four classes in host population representing susceptible host (S h) , exposed host (E h) , asymptomatic host (A h) and infectious host (I h) whereas in vector susceptible (S v) and infectious (I v) class are present. Activation function is introduced for the formulation of the fitness-based function as mean squared error by using nonlinear PWD equations for the accomplishment of ANNs-GASQP paradigm. The stability, robustness and effectiveness of proposed paradigm is comparatively evaluated through Adam numerical scheme with absolute error analysis. Computational complexity of GASQP is determined by convergence criteria of best global weight, fitness evaluation, time, generations, iterations, function counts and mean square error. Moreover, the statistical analysis is performed via Theil's inequality coefficients (TICs), mean of absolute deviation (MAD) and root mean squared error (RMSE) for multiple trials of ANNs-GASQP. Results reveal that accuracy is obtained up to 3–11 decimal places which proves the reliability of proposed ANNs-GASQP solver. [ABSTRACT FROM AUTHOR]

This study explores the Entropy generation in the Flow of Jeffery Nanomaterial Model (EFJNM) past a variable thicked surface by considering joule heating effect, convectively heated surface, nonlinear radiative heat flux, and convective boundary conditions via Artificial Back Propagated Levenberg Marquardt Neural Networks (ABP-LMNNs). The ODEs system is obtained by implementing suitable transformation on PDEs for EFJNM. Applying homotopy analysis method (HAM) to create dataset for ABP-LMNNs by the variation of physical parameters of EFJNM. The reference datasets are then segmented in validation/training/testing process to scrutinize the approximated solution for EFJNM and further to compare it with standard solution. The better performance is further investigated by MSE based curves, regression fitting, absolute error, error histogram analysis. Results reveals that performance in form of MSE is in ranges of 10−9, 10−11, 10−9, 10−9, 10−10, 10−9, 10−10, 10−9, and 10−10 with 494, 285, 496, 219, 71, 167, 194, 121, 179, and 738, respectively, for all scenarios. The impact of variants of EFJNM on velocity, entropy production rate, temperature, and concentration are also analyzed. As the entropy generation increase with the increment in Deborah number and Brinkman number. The velocity increases with the escalation in Deborah number and wall thickness variable whereas decreases with the increase in relaxation to retardation ratio. The absolute errors of effective parameters for velocity are 10−7-10−4, 10−7-10−3, 10−7-10−4, and 10−6-10−3, the absolute errors for temperature are 10−7-10−3, 10−7-10−3, and 10−7-10−4 whereas the absolute errors for concentration are 10−8-10−4, 10−8-10−3, and 10−8-10−4, which show the accuracy of proposed ABP-LMNNs. Abbreviations: ABP-LMNNs, Artificial back propagated Levenberg Marquardt Neural Networks; EFJNM, Entropy generation in the Flow of Jeffery Nanomaterial Model; HAM, Homotopy analysis method [ABSTRACT FROM AUTHOR]

Spinal anesthesia still a very common method of anesthesia for surgery on the torso below the neck; but it is known to be associated with some setbacks / complications, e.g., failed spinal, spinal hypotension, patch effect with missed dermatomes, post-spinal headache etc. The author presents a rare occurrence of spinal needle being broken during the procedure, and the distal part being removed by surgical exploration. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To determine the efficacy of pain for Ultrasound-Guided Transversus Abdmominis Plane Block versus landmark technique in lower abdominal surgery. METHODOLOGY: This Randomized Control trial study was conducted from July to December 2018 at the Department of Anesthesiology, Peoples Medical University & Hospital Shaheed Benazirabad. The sample techniques were used randomly through envelopes, and the sample size was 120. RESULTS: The study findings revealed that age distribution among L and U groups regarding gender was 70% females 66.7% females. Moreover, for ASA status in the L group, 58.3% of the participants stand in 2-4 ASA status, whereas in the U group, 50% of the participants were in ASA status 1 and 2-4 each. The bilateral block was performed among 85% of the participants in the L group and 96.7% of the participants in the U group. In the surgical procedure in the L group, 15% underwent appendectomy, whereas, in the U group, 28.3% underwent lower c-section. The overall rate of postoperative pain at 60 minutes was observed in 10 women among both groups. Statistically significant results among both groups with a p-value of 0.001. CONCLUSION: Our study results showed that surgeries performed using Ultrasound-Guided Transversus Abdominis Plane Block are more effective with less postoperative analgesia. [ABSTRACT FROM AUTHOR]

Objectives: To compare the efficacy of bupivacaine 2.5 mg and fentanyl 25 µg with bupivacaine 1.25 mg and fentanyl 25µg for spinal-epidural analgesia in the first stage of labor. Study Design: Double-blind randomized controlled trial. Setting: Anesthesia Department, Surgical Intensive Care Unit, and Pain management clinic, Peoples Medical College Hospital Nawabshah. Period: August 2014 to July 2015. Methodology: All the participants meeting the eligibility criteria were randomly allocated into two groups i.e. intervention (I) and control (II), with the allocation ratio of 1:1. Patients in the Group I (intervention) received intrathecal Inj. Bupivacaine 1.25 mg (0.5% Bupivacaine 0.25ml) and Inj. Fentanyl 25 µg whereas the Group II (control) was given intrathecal Inj. Bupivacaine 2.5 mg (0.5% Bupivacaine 0.5ml) and Inj. Fentanyl 25 µg for combined spinal epidural analgesia, both made up of total volume of 2 ml of normal saline. Mean±SD (standard deviation) was computed for continuous data (age, weight, VAS). Frequency and percentages was calculated categorical data. Independent t test and Chi square test were used for the differences between the groups. Results: The age of all the cases was 27.64±4.07 years. Moreover, groups were homogenous at baseline (p<0.05) in terms of mean age, weight, cervical dilatation, gravida, and ASA classification. Mean pain score on VAS was not significant between groups at 0 min and 15 min however mean pain score was found significantly low (p<0.05) in group-I. Likewise, efficacy of Bupivacaine 1.25 mg and Fentanyl 25 µg (group-I) was significantly higher than the other group. Conclusion: Low-dose bupivacaine 1.25 mg was significantly more effective than high-dose (2.5 mg) bupivacaine when added to 25 µg of fentanyl for combined spinal-epidural analgesia in the first stage of labor, having fewer chances of sensory and motor block, and hypotension. [ABSTRACT FROM AUTHOR]

Objective: The recent interest in intravenous magnesium sulphate for using it in many other indications other than control of eclamptic fits lead the researchers to try it as a smooth muscle relaxant in cases of acute bronchial asthma. The results have largely been controversial, so we conducted this study to determine efficacy of intravenous magnesium sulphate in acute asthma based on its terminal outcome. Methodology: This retrospective study was conducted at Surgical Intensive Care Unit at Peoples University of Medical and Health Sciences for Women, Nawabshah from January 2014-December 2015. We retrospectively reviewed the medical data sheets of all cases of acute asthma admitted during the study period, and extracted the relevant information. All the patients were initially treated with standard therapy for asthma and then given 4ml of 50% MgSO4 (2g) diluted in 250 ml of normal saline intravenously. Outcomes were presented in percentages and frequencies while patient age is presented as mean ± SD. Results: The mean age of the population was 45.29 ± 20.1 y. We had a female predominance in our population (60.3%). Successfully cured patients were 32.3% whereas those expired were 57.4%. There was not a significant difference between the genders for the outcome (p > 0.05). Conclusions: We found that intravenous magnesium sulphate is ineffective in successfully management of patients with acute asthma in terms of enhanced survival rate. [ABSTRACT FROM AUTHOR]

This article addressed entropy generation of Nonlinear Electro-Magneto Mixed Convective nanofluid model (NEM-MCNM) with second-order velocity slip flow over a stretched surface via artificial backpropagated neural networks with Levenberg–Marquardt scheme (ABPNN-LMS). The ODEs are formed by applying suitable similarity transformation on system of PDEs for NEM-MCNM. The reference dataset is obtained by the variation of different parameters from homotopy analysis method (HAM) for all scenarios of proposed ABPNN-LMS. The reference data samples for training, testing and validation sets are used to find the approximate solution of proposed ABPNN-LMS and to compare it with reference HAM outcomes. The worthy and consistent performance of ABPNN-LMS is certified through MSE convergence, and the error histogram and regression index in terms of ABPNN-LMS are also analyzed. The impact of all variants on velocity, thermal gradient, concentration, skin friction, Bejan number, entropy generation, Sherwood and Nusselt number is discussed by conducting graphics and numerical studies. Results reveal that the decrease in motion of the fluid is seen for higher slip parameters. The temperature acts as an increasing function of Eckert number and thermophoresis parameter. The augmentation in activation energy enhances the concentration of the fluid, whereas reverse trend is noticed for reaction parameter. The absolute error results are obtained as 10–07–10–03 and 10–07–10–04 for velocity, 10–07–10–04 and 10–07–10–03 for temperature and 10–09–10–03 and 10–06–10–03 which determine the accuracy of ABPNN-LMS. The best performance is accomplished at, 265, 250, 636, 346, 299 and 182 epochs with MSE around E - 09 , E - 10 , E - 09 , E - 09 , E - 09 and E - 08 . [ABSTRACT FROM AUTHOR]

Background: Globally, over a million people commit suicide every year. Although suicide rates are more in high-income countries, many countries do not report suicide cases regularly to the World Health Organization (WHO). Therefore, this study aimed to determine the factor associated with suicide and self-harm in Pakistan. Method: This cross-sectional study was conducted at Peoples Medical College Hospital (PMCH) in Shaheed Benazirabad, Sindh, Pakistan, from July to December 2019. A total of 131 cases of suicide/self-harm were included using a convenience sampling technique. Data were collected on a predesigned questionnaire consisting of 14 close-ended questions. A chi-square test was used to determine the association between different categorical variables. Results: The majority of the subjects were males (53.4%), young adults aged between 16 and 30 years (69.5%), single (51.9%), and uneducated (57.3%). More than half (51.9%) of the subjects who attempted suicide or self-harm were unemployed. There was a significant association between education level (Χ² =13.149, P = 0.001) and age groups (Χ² = 15.554, P = 0.001) with health outcomes (suicide or self-harm) only. Moreover, gender (Χ² = 20.776, P = 0.004), marital status (Χ² = 69.047, P < 0.001), level of education (Χ² = 63.144, P < 0.001), age groups (Χ² = 69.848, P < 0.001), and employment status (Χ² = 28.677, P = 0.012) were also associated with the reasons of suicide and self-harm. Conclusion: Our study concluded that mostly single, unemployed males with low literacy and with marital and family issues are determined as factors associated with a high risk of self-harm and suicide. [ABSTRACT FROM AUTHOR]

In the present article, the intelligence based numerical investigation by Levenberg–Marquardt algorithm with Back‐propagated Artificial Neural Networks (LMA–BANN) is exploited to analyze the nonlinear radiative stagnation point flow of cross nanofluid system (NRS‐CNFS) past a stretching surface. The designed NRS‐CNFS, initially represented by system of partial differential equations (PDEs), is converted into system of non‐linear ODEs through the applicability of mathematical conversion analysis. The desired reference solution in the form of a dataset for LMA–BANN is achieved from the Adam method for NRS‐CNFS's different scenarios by varying the magnetic parameter (Ha), Prandlt number (Pr), thermal radiation parameter(R), Eckert number (Ec), thermophoresis diffusion coefficients (Nt), and non‐dimensional activation energy (E). The obtained results are construed for the NRS‐CNFS through the performances of the testing, validation, and training. In addition, the comparison is provided using the LMA ‐BANN analysis, which is validated in terms of regression, fitness estimations, and histograms along with MSE performances. Hence, the article provides an impeccable innovative approach to problem‐solving by implementing a soft computation paradigm. It promotes the use of an effective and dependable alternative framework that is based on soft computing environments and emphasizes descriptive analysis to successfully handle the problems brought on by varied physical features. [ABSTRACT FROM AUTHOR]

This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the intelligent computing paradigm via artificial Levenberg–Marquardt back propagated neural networks (ALM-BPNNs). The governing partial differential equations (PDEs) of MHD-VFFM are transformed into ODEs by applying suitable similarity transformations. The reference dataset is obtained from Adam numerical solver by the variation of Hartmann number (Ha), thickness parameter (α) , power index (n) , thermophoresis parameter (Nt), Brinkman number (Br), Lewis number (Le) and Brownian diffusion parameter (Nb) for all scenarios of proposed ALM-BPNN. The reference data samples arbitrary selected for training/testing/validation are used to find and analyze the approximated solutions of proposed ALM-BPNNs as well as comparison with reference results. The excellent performance of ALM-BPNN is consistently endorsed by Mean Squared Error (MSE) convergence curves, regression index and error histogram analysis. Intelligent computing based investigation suggests that the rise in values of Ha declines the velocity of the fluid motion but converse trend is seen for growing values of n. The rising values of Ha, Nt and Br improve the heat transfer but converse trend is seen for growing values of α. The inclining values of Nt incline the mass transfer but it shows reverse behavior for escalating values of Le. The inclining values of Br incline the EP. [ABSTRACT FROM AUTHOR]

[Display omitted] • Concepts and definitions. • Fluid dynamics. • Non-viscous flow. • Conservation of mass and energy. • Darcy-Forchheimer Flow. • Entropy generation. • Artificial neural networks with Levenberg-Marquardt backpropagation • Molybdenum disulfide and silicon dioxide nanoparticles • Thermal radiation • Heat generation • Numerical solutions The work in hand examines the entropy generation of magnetohydrodynamic Darcy-Forchheimer nanofluid flow model (MHD-DFNM) over a stretched surface via Artificial back propagated neural networks with Levenberg Marquardt Algorithm (ABNN-LMA). Two types of nanoparticles i.e Silicon dioxide (SiO 2) and Molybdenum disulfide (MoS 2) and continuous phase liquid i.e propylene glycol is considered. The PDEs of MHD-DFNM are transformed into coupled ODEs via appropriate (similarity) transformations. The reference dataset is obtained by the variation of 1st and 2nd order slip parameters (γ 1 and γ 2), porosity parameter (β), viscosity parameter (λ), radiation parameter(R), volume friction coefficient(ϕ), heat generation/absorption parameter(Q), Biot number (B 1) and Eckert number (Ec) from Homotopy Analysis Method (HAM). The reference data is executed in training/testing/validation sets to find and analyze the approximated solution of designed ABNN-LMA as well as it's comparison with reference data solution. The better performance is consistently certified with Mean Squared Error (MSE) curves, regression index and error histogram study. Results reveal that, increase in values of porosity parameter declines the velocity profile for both SiO 2 and MoS 2 nanoparticles suspended in propylene glycol nanofluids. The thermal profile improves for both SiO 2 − propylene glycol and MoS 2 − propylene glycol nanofluids when heat generation parameter increases. [ABSTRACT FROM AUTHOR]

COVID-19 is the current topic of discussion globally as people are getting affected by it on a huge scale. This study is focused to determine the concerns and perceptions of healthcare workers (HCWs) due to the COVID-19 pandemic and its effect on their mental health, routine work, family and social life. Study was conducted at various health care facilities of Sindh, Pakistan, from October to December 2020 (three months). An online survey questionnaire consisting of fourteen closed-ended questions was designed in Google Forms and circulcalted among the HCWs through email and social media. The data collected was analyzed using SPSS 24 and descriptive statistical tools were used to measure the frequencies and the Chi-square test was applied among correlated variables. Among 412 respondents, majority of the participants were male (54.6%) and young with 18-28 years of age (47.3%). Two-third of HCWs were highly concerned about their family's health versus own health (67.7% vs 44.7% respectively) and 157 (38.1%) were emotionally distressed. It was also found that HCWs with assigned duties in the isolation wards were more emotionally distressed (56.2% high to very high) compared to those not working in isolation units (45.3% high to very high). More than half of HCWs (51.9%) reported that their family life was also disturbed. Our findings indicate that COVID-19 pandemic has a significant psychological impact on frontline soldiers (HCWs) particularly they were worried about family's health. The HCWs who were assigned duties in isolation units were more emotionally distressed than those who were not assigned duties in isolation wards. [ABSTRACT FROM AUTHOR]

Objective: Suicidal victims often reach Intensive Care Units (ICU), if they manage to reach a hospital, and the anesthesiologist get involved in their management. We aimed to evaluate the incidence of suicidal patterns, methods being used and seasonal variations in the frequency of suicide victims admission at Intensive Care Unit (ICU) of Peoples Medical College Hospital (PMCH), Nawabshah (Pakistan). Study design: Retrospective study Methodology: A cohort study was conducted over a four and a half years (January 2014 to August 2018), including suicidal cases of both sexes received at ICU, PMCH, Nawabshah, from six districts of interior Sindh. Cases were selected on the basis of information from hospital record files after the approval of hospital ethical review committee. Findings were tabulated and analysed. Results: This study revealed that out of a total of 685 cases, 391 (57.08%) were males and 294 (42.92%) were females. Eighty males (11.67%) and 68 (9.93%) females could not be survived while two cases (0.29%) were referred to Karachi during the study period. The highest incidence of suicidal attempts and suicidal deaths were 164 and 38 during 2017 and 2014 respectively. Males were the predominant victims of suicide with a male to female ratio of 2:1.7. The most common method used for suicide was poisoning 98.77% (n = 656) followed by hanging 3.21% (n = 22) and drug overdose 1.02% (n = 07). A seasonal surge in autumn (August 17.81%) was noted. Conclusion: Males were the predominant victims of suicide in rural areas of interior Sindh Province of Pakistan. The most common method used for suicide was poisoning. [ABSTRACT FROM AUTHOR]

The major purpose of this article is to exploit the strength of the Intelligent Back-propagated Neural Networks of Levenberg Marquardt Technique (IBNN-LMT) to study three-dimensional (3D) squeezed flow model of carbon nanotubes (SFM-CNTs) relying on water in a revolving network with a rigid ground wall. The impact of magnetohydrodynamic (MHD) and thermal radiation has been investigated. Similarity transformation is utilized to convert partial differential equations (PDEs) into ordinary differential equations (ODEs). For numerous nanofluids constructed of SWCNT and MWCNT, the influence of each parameter on velocity, temperature, Bejan number, and entropy production is estimated. The overall entropy optimization was also computed. The major outcome of this research is to examine the impacts of flowing parameters on CNTs when squeezing parameters are changed. Moreover, the estimated solution is evaluated for designed SFM-CNTs by using a testing method and comparing it to a standard solution that has been backed up by performance studies based on MSE convergence, error histogram, and regression studies. The velocity profile f (χ) along y-axis intensifies with the intensification in S , φ ,and A. Where Ω gives a dual performance with the increment in the function, as with the increase in M , f (χ) provides decreasing performance. The temperature function θ (χ) increase as the increment in S , Ec ,and Ec m and decrease with the increase in φ and R. The solution is accurate up to 4 to 10 decimal places which proves the worth and effectiveness of the proposed IBNN-LMT solver. [ABSTRACT FROM AUTHOR]