Early Outcome of Simultaneous Bilateral Total Knee Arthroplasty Through the Oxford Knee Score in a Developing Country: A Prospective Cohort.

Introduction The most common degenerative joint disease in adults is osteoarthritis. The gold standard treatment option for this aging society with greater functional demands is total knee arthroplasty. The Oxford Knee Score (OKS) assesses factors such as stiffness, pain, function, satisfaction, and quality of life, allowing you to quantify treatment success after surgery. According to published research, there isn't a perfect postoperative timepoint to use the OKS to obtain TKA outcomes. Therefore, at the time of evaluation, the OKS should reflect the best possible outcome for the patient group. This study was conducted to see the OKS in patients who underwent simultaneous bilateral knee replacement at six weeks and six months postoperatively and to see if there was a clinically significant difference in the mean OKS. Methods This prospective cross-sectional study was conducted at the Section of Orthopedic Surgery at Aga Khan University Hospital, a tertiary care center in Karachi, Pakistan. Patients who underwent simultaneous bilateral total knee replacement from October 2023 till December 2023 were included; exclusion criteria included patients who had a recent knee infection and extensor mechanism disruption. OKS was calculated at six weeks and six months postoperatively. Results The total number of patients included in the study was 49 with a mean age of 61.9 +/- 6.1. There were 42 (85.7%) females and 7 (14.3%) males. The mean BMI of our patients was 33.3 +/- 3.8. The radiographic Kellgren Lawrence Grading (KLG) was used and 38 (77.6%) patients were placed in Grade IV KLG, and 11 (22.4%) were placed in Grade III KLG. The mean OKS preoperatively was 12.6 +/- 3.5. At six weeks, the OKS showed improvement, with the mean being 20.6 +/- 3.0. At six months postoperatively, there was a significant improvement in the OKS, with the mean now being 42.7 +/- 2.4. At six weeks post-surgery, the mean improvement in the OKS score was 7.9 +/- 2.71, whereas at six months post-surgery, the mean improvement in the OKS score was 30.1 +/- 3.6. This difference was significant (p-value=0.03). Conclusion Our study showed a clinically significant difference between the mean OKS at the six-week and six-month timeline, with a significant increase in the mean improvement OKS score at six months. OKS should be utilized six months postoperatively to assess the outcome of simultaneous bilateral knee arthroplasty patients.

Perspective on Clinical and Functional Outcomes of Arthroscopic All-Inside Meniscal Repair: Insights From a Lower Middle-Income Country.

Background Meniscal tears are a common injury in the adult population. With the advent of newer devices, the adoption of the all-inside repair technique has been expanding substantially because of its feasibility and reduced risk to surrounding neurovascular structures. This study was conducted in a lower middle-income country to assess the functional outcome of the arthroscopic all-inside technique and to identify the potential factors that may affect the functional outcome that will eventually influence the future management of these patients. Method This study is a retrospective case series conducted at the Department of Orthopedic Surgery, Aga Khan University Hospital, Karachi, Pakistan. Patients presenting to the outpatient clinics with meniscal tears who underwent arthroscopic all-inside repair from January 2015 to December 2021 were included in this study. The exclusion criteria included patients who had associated fractures and patients with meniscal tears greater than six months ago. Results A total of 29 patients underwent all-inside meniscal repair for meniscus tears. The mean age of our patients was 26.31 years (SD = 7.11 years), ranging from 17 years to 48 years. Of these patients, 26 were males and three were females, accounting for 89.7% and 10.3%, respectively. The most frequent mechanism of injury was twisting while playing sports, accounting for 51.7%, followed by falling while playing sports and road traffic accidents (RTAs), accounting for 13.8% and 20.7%, respectively. Of the 29 patients, 16 (55.2%) had lateral meniscal injuries, 10 (34.5%) were diagnosed with medial meniscus injuries, and three (10.3%) had injuries to both menisci. The most common type of tear that was observed in our sample size was bucket handle tears, which were found in 14 patients, accounting for a total of 48.3%, followed by complex tears in seven patients (24.1%). The majority of the patients, i.e., 19 out of 29 patients (65.5%), had an acute course of injury, i.e., less than six weeks. For the functional outcome, the Lysholm score was calculated at 12 months and was found to be excellent in 17 patients, good in six patients, and fair in six patients, accounting for 58.6%, 20.7%, and 20.7%, respectively. The mean Lysholm score was 90.03 ± 8.85 points. Of the 29 patients, 27 (93.2%) had no complaints at the regular 12-month follow-up, whereas one patient (3.4%) experienced rotatory instability and one patient (3.4%) experienced stiffness at the knee joint. None of the patients had to undergo a reoperation. The mean Lysholm score in the 25 patients who had an associated anterior cruciate ligament tear was 89.64 ± 9.442 points, whereas the four patients who had an isolated meniscal tear had a mean score of 92.50 ± 2.887 points, which was not significantly different (p-value = 0.831). Conclusion All-inside meniscal repair for treating meniscal tears has become the new treatment paradigm as it not only renders excellent functional outcomes with minimal complications but also prevents damage to the surrounding neurovasculature and the soft tissue envelope as it is a minimally invasive technique.

Mathematical modeling and machine learning-based optimization for enhancing biofiltration efficiency of volatile organic compounds.

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

Influence of bite force and implant elastic modulus on mandibular reconstruction with particulate-cancellous bone marrow grafts healing: An in silico investigation.

This study aims to investigate tissue differentiation during mandibular reconstruction with particulate cancellous bone marrow (PCBM) graft healing using biphasic mechanoregulation theory under four bite force magnitudes and four implant elastic moduli to examine its implications on healing rate, implant stress distribution, new bone elastic modulus, mandible equivalent stiffness, and load-sharing progression. The finite element model of a half Canis lupus mandible, symmetrical about the midsagittal plane, with two marginal defects filled by PCBM graft and stabilized by porous implants, was simulated for 12 weeks. Eight different scenarios, which consist of four bite force magnitudes and four implant elastic moduli, were tested. It was found that the tissue differentiation pattern corroborates the experimental findings, where the new bone propagates from the superior side and the buccal and lingual sides in contact with the native bone, starting from the outer regions and progressing inward. Faster healing and quicker development of bone graft elastic modulus and mandible equivalent stiffness were observed in the variants with lower bite force magnitude and or larger implant elastic modulus. A load-sharing condition was found as the healing progressed, with M3 (Ti6Al4V) being better than M4 (stainless steel), indicating the higher stress shielding potentials of M4 in the long term. This study has implications for a better understanding of mandibular reconstruction mechanobiology and demonstrated a novel in silico framework that can be used for post-operative planning, failure prevention, and implant design in a better way.

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials.

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge-Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Isoliquiritin Ameliorates Ulcerative Colitis in Rats through Caspase 3/HMGB1/TLR4 Dependent Signaling Pathway.

BACKGROUND: Isoliquiritin belongs to flavanol glycosides and has a strong antiinflammatory activity. This study sought to investigate the anti-inflammatory effect of isoliquiritin and its underlying mechanism. METHODS: The inflammatory (trinitro-benzene-sulfonic acid-TNBS-induced ulcerative colitis (UC)) model was established to ascertain the effect of isoliquiritin on the caspase-3/HMGB1/TLR4 pathway in rats. We also explored its protective effect on intestinal inflammation and its underlying mechanism using the LPS-induced inflammation model of Caco-2 cells. Besides, Deseq2 was used to analyze UCassociated protein levels. RESULTS: Isoliquiritin treatment significantly attenuated shortened colon length (induced by TNBS), disease activity index (DAI) score, and body weight loss in rats. A decrease in the levels of inflammatory mediators (IL-1ß, I IL-4, L-6, IL-10, PGE2, and TNF-α), coupled with malondialdehyde (MDA) and superoxide dismutase (SOD), was observed in colon tissue and serum of rats after they have received isoliquiritin. Results of techniques (like western blotting, real-time PCR, immunohistochemistry, and immunofluorescence-IF) demonstrated the potential of isoliquiritin to decrease expressions of key genes in the TLR4 downstream pathways, viz., MyD88, IRAK1, TRAF6, NF-κB, p38, and JNK at mRNA and protein levels as well as inhibit HMGB1 expression, which is the upstream ligand of TLR4. Bioinformational analysis showed enteritis to be associated with a high expression of HMGB1, TLR4, and caspase-3. CONCLUSION: Isoliquiritin could reduce intestinal inflammation and mucosal damage of TNBS-induced colitis in rats with a certain anti-UC effect. Meanwhile, isoliquiritin treatment also inhibited the expression of HMGB1, TLR4, and MyD88 in LPS-induced Caco-2 cells. These results indicated that isoliquiritin could ameliorate UC through the caspase-3/HMGB1/TLR4-dependent signaling pathway.

Investigation of tribo-mechanical performance of alkali treated rice-husk and polypropylene-random-copolymer based biocomposites.

This study was based on the experimental performance evaluation of a wood polymer composite (WPC) that was synthesized by incorporating untreated and treated rice husk (RH) fibers into a polypropylene random copolymer matrix. The submicron-scale RH fibers were alkali-treated to modify the surface and introduce new functional groups in the WPC. A compatibilizer (maleic anhydride) and a thermos-mechanical properties modifier (polypropylene grafted with 30 % glass fiber) were used in the WPC. The effects of untreated and treated RH on the WPC panels were studied using FESEM, FTIR, and microscope images. A pin-on-disk setup was used to investigate the bulk tribological properties of PPRC and WPC. The complex relationship between the friction coefficient of different loading of RH fibers in the WPC, as a function of sliding distance, was analyzed along with the temperature and morphology of the surface. It was observed that untreated RH acted as a friction modifier, while treated RH acted as a solid lubricant. Microhardness was calculated using the QCSM module on nanoindentation. It was found that untreated RH led to an increase in microhardness, while treated RH caused a decrease in hardness compared to PPRC.

Paucity of Health Data in Africa: An Obstacle to Digital Health Implementation and Evidence-Based Practice.

Background: Among the numerous challenges that Africa faces in improving its healthcare systems, the paucity of health data stands out as paramount. This study aims to examine the challenges related to the paucity of health data in Africa and its impact on the implementation of digital health and evidence-based practice. The findings of the study reveal that health data availability in Africa is both limited and frequently of poor quality. Several factors contribute to this concerning situation, encompassing inadequate infrastructure, a shortage of resources, and cultural barriers. Furthermore, the available data, despite its limitations, is often underutilized due to a lack of capacity and expertise in data analysis and interpretation. Policy Options and Recommendations: To improve healthcare delivery in Africa, we recommend implementing novel strategies for data collection. It's important to recognize that effective information technology service is crucial for enhancing healthcare delivery, and a holistic approach is necessary to achieve this. Conclusion: This brief presents information to help policymakers develop long-term solutions to Africa's health data poverty. Taking action based on this evidence can assist in addressing the problem.

Quantitative Study of Non-Linear Convection Diffusion Equations for a Rotating-Disc Electrode.

Rotating-disc electrodes (RDEs) are favored technologies for analyzing electrochemical processes in electrically charged cells and other revolving machines, such as engines, compressors, gearboxes, and generators. The model is based on the concept of the nonlinear entropy convection-diffusion equations, which are constructed using semi-boundaries as an infinite notion. In this model, the surrogate solutions with different parameter values for the mathematical characterization of non-dimensional OH- and H+ ion concentrations at a rotating-disc electrode (RDE) are investigated using an intelligent hybrid technique by utilizing neural networks (NN) and the Levenberg-Marquardt algorithm (LMA). Reference solutions were calculated using the RK-4 numerical method. Through the training, validation, and testing sampling of reference solutions, the NN-BLMA approximations were recorded. Error histograms, absolute error, curve fitting graphs, and regression graphs validated the NN-BLMA's resilience and accuracy for the problem. Additionally, the comparison graphs between the reference solution and the NN-BLMA procedure established that our paradigm is reliable and accurate.

Clinical audit of the management and outcomes of preterm pre-labour rupture of membranes at Aminu Kano Teaching Hospital.

Introduction: preterm pre-labour rupture of membranes (PPROM) is one of the important causes of preterm birth that can result in high perinatal morbidity and mortality along with maternal morbidity. The purpose of the study was to audit the management of women presenting with Preterm pre-labour rupture of membranes in Aminu Kano Teaching Hospital (AKTH). Methods: this was a retrospective audit on patients admitted with PPROM in AKTH over a period of 24 months. Data was analysed using SPSS version 22 and presented using percentages and compared with the audit standard. Chi-squared test was used to test for association (p-value <0.05). Results: the mean gestational age was 33.27±2.42 weeks. Diagnosis was made on all patients through history and clinical examination. Almost all patients received a course of erythromycin (88%), corticosteroid (84%) and magnesium sulphate (86%). Vaginal delivery was achieved in 57%. About 60% of the neonates were premature, 78% had Apgar score >7 at 5 mins, 50% were admitted in the special care baby unit and 72% survived. Chorioamnionitis and puerperal sepsis occurred in 8% and 21.7% of the mothers. Prolonged PPROM of >24 hours was statistically significantly associated with puerperal sepsis (χ2=7.218; p = 0.007) and perinatal mortality (χ2= 11.505, p = 0.001). Conclusion: despite high fidelity to institutional clinical practice guidelines in Aminu Kano Teaching Hospital there seems to be poor maternal and neonatal outcome with high perinatal mortality. Thus the guidelines need to be reviewed in context of improving the outcome.

Analysis of heat transmission in convective, radiative and moving rod with thermal conductivity using meta-heuristic-driven soft computing technique.

Abstract: The present study analyzes the thermal attribute of conductive, convective, and radiative moving fin with thermal conductivity and constant velocity. The basic Darcy's model is utilized to formulate the governing equation for the problem, which is further nondimensionalized using certain variables. Moreover, an effective soft computing paradigm based on the approximating ability of the feedforword artificial neural networks (FANN's) and meta-heuristic approach of global and local search optimization techniques is developed to quantify the effect of variations in significant parameters such as ambient temperature, radiation-conduction number, Peclet number, nonconstant thermal conductivity, and initial temperature parameter on the temperature gradient of the rod. The results by the proposed FANN-AOA-SQP algorithm are compared with radial basis function approximation, Runge-Kutta-Fehlberg method and machine-learning algorithms. An extensive graphical and statistical analysis based on solution curves and errors such as absolute errors, mean square error, standard deviations in Nash-Sutcliffe efficiency, mean absolute deviations, and Theil's inequality coefficient are performed to show the accuracy, ease of implementation, and robustness of the design scheme.

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach.

Most plant viral infections are vector-borne. There is a latent period of disease inside the vector after obtaining the virus from the infected plant. Thus, after interacting with an infected vector, the plant demonstrates an incubation time before becoming diseased. This paper analyzes a mathematical model for persistent vector-borne viral plant disease dynamics. The backpropagated neural network based on the Levenberg-Marquardt algorithm (NN-BLMA) is used to study approximate solutions for fluctuations in natural plant mortality and vector mortality rates. A state-of-the-art numerical technique is utilized to generate reference data for obtaining surrogate solutions for multiple cases through NN-BLMA. Curve fitting, regression analysis, error histograms, and convergence analysis are used to assess accuracy of the calculated solutions. It is evident from our simulations that NN-BLMA is accurate and reliable.

Monkeypox in human pregnancy: an overview.

Monkeypox is a viral zoonosis caused by the monkeypox virus, an enveloped, double-stranded DNA virus belonging to the Orthopoxvirus genus in the Poxviridae family. Monkeypox has become a disease of global public health significance. Pregnant women are unfortunately among the those at an increased risk for exposure to monkeypox because their immune system is altered during pregnancy. They may also be at risk for more severe disease or a worse outcome than others. During pregnancy or while breastfeeding when consideration is given for pre-exposure or postexposure vaccination, nonreplicating (Modivied Vaccinia Ankara - Bavarian Nordic) or minimally replicating (LC16, KM Biologics) vaccines are preferred. The ACAM2000 vaccine is contraindicated in pregnancy because it contains live virus particles that can cause fetal vaccinia and fetal death. There are no data to support the use of tecovirimat in pregnant women. However, no fetal adverse effects were noticed when tecovirimat was used in animal studies.

SRAM-Based CIM Architecture Design for Event Detection.

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the high computational complexity and high-energy consumption of CNNs trammel their application in hardware accelerators. Computing-in-memory (CIM) is the technique of running calculations entirely in memory (in our design, we use SRAM). CIM architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. CIM-based architecture for event detection is designed to trigger the next stage of precision inference. To implement an SRAM-based CIM accelerator, a software and hardware co-design approach must consider the CIM macro's hardware limitations to map the weight onto the AI edge devices. In this paper, we designed a hierarchical AI architecture to optimize the end-to-end system power in the AIoT application. In the experiment, the CIM-aware algorithm with 4-bit activation and 8-bit weight is examined on hand gesture and CIFAR-10 datasets, and determined to have 99.70% and 70.58% accuracy, respectively. A profiling tool to analyze the proposed design is also developed to measure how efficient our architecture design is. The proposed design system utilizes the operating frequency of 100 MHz, hand gesture and CIFAR-10 as the datasets, and nine CNNs and one FC layer as its network, resulting in a frame rate of 662 FPS, 37.6% processing unit utilization, and a power consumption of 0.853 mW.

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach.

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10-4 to 10-5 and 10-8 to 10-10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme's results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

Tackling Cholera Outbreak Amidst COVID-19 Pandemic in Nigeria: Challenges and Recommendations.

Background: Since the first confirmed case of the Novel Coronavirus Disease 2019 (COVID-19) in Nigeria, the pandemic has become a major public health challenge, affecting different sectors of the country. While Nigeria is battling to control the spread of COVID-19, the eruption of new cholera cases has put additional pressure on the strained healthcare system. Evidence: We showed how the overwhelming focus on COVID-19 has jeopardized key cholera containment measures such as disease surveillance, risk communication, and case management. Policy Options and Recommendations: We recommend provision and universal access to safe water and sanitation as key cholera preventive and control measures. However, these are resources that developing countries including Nigeria find rather challenging to provide. We also proposed the implementation of well-coordinated multi-sectoral interventions that involve strengthening disease surveillance including access to safe drinking water, vaccines, and treatment, especially in vulnerable communities. Conclusion: This policy brief provides evidence for policymakers, which if acted upon, will foster sustainable solutions to the lingering cholera outbreaks in Nigeria.

Exploring use of a commercial passive sampler in a closed static chamber to measure ammonia volatilization.

Studies have indicated that up to 47% of total N fertilizer applied in flooded rice fields may be lost to the atmosphere through NH3 volatilization. The volatilized NH3 represents monetary loss and contributes to increase in formation of PM2.5 in the atmosphere, eutrophication in surface water, and degrades water and soil quality. The NH3 is also a precursor to N2O formation. Thus, it is important to monitor NH3 volatilization from fertilized and flooded rice fields. Commercially available samplers offer ease of transportation and installation, and thus, may be considered as NH3 absorbents for the static chamber method. Hence, the objective of this study is to investigate the use of a commercially available NH3 sampler/absorbent (i.e., Ogawa® passive sampler) for implementation in a static chamber. In this study, forty closed static chambers were used to study two factors (i.e., trapping methods, exposure duration) arranged in a Randomized Complete Block Design. The three trapping methods are standard boric acid solution, Ogawa® passive sampler with acid-coated pads and exposed coated pads without casing. The exposure durations are 1 and 4 h. Results suggest that different levels of absorbed NH3 was obtained for each of the trapping methods. Highest level of NH3 was trapped by the standard boric acid solution, followed by the exposed acid-coated pads without casing, and finally acid-coated pads with protective casing, given the same exposure duration. The differences in absorbed NH3 under same conditions does not warrant direct comparison across the different trapping methods. Any three trapping methods can be used for conducting studies to compare multi-treatments using the static chamber method, provided the same trapping method is applied for all chambers.

Phantom and Animal Study of a Robot-Assisted, CT-Guided Targeting System using Image-Only Navigation for Stereotactic Needle Insertion without Positional Sensors.

PURPOSE: To evaluate the feasibility and accuracy of a robotic system to integrate and map computed tomography (CT) and robotic coordinates, followed by automatic trajectory execution by a robotic arm. The system was hypothesized to achieve a targeting error of <5 mm without significant influence from variations in angulation or depth. MATERIALS AND METHODS: An experimental study was conducted using a robotic system (Automated Needle Targeting device for CT [ANT-C]) for needle insertions into a phantom model on both moving patient table and moving gantry CT scanners. Eight spherical markers were registered as targets for 90 insertions at different trajectories. After a single ANT-C registration, the closed-loop software targeted multiple markers via the insertion of robotically aligned 18-gauge needles. Accuracy (distance from the needle tip to the target) was assessed by postinsertion CT scans. Similar procedures were repeated to guide 10 needle insertions into a porcine lung. A regression analysis was performed to test the effect of needle angulation and insertion depth on the accuracy of insertion. RESULTS: In the phantom model, all needle insertions (median trajectory depth, 64.8 mm; range, 46.1-153 mm) were successfully performed in single attempts. The overall accuracy was 1.36 mm ± 0.53, which did not differ between the 2 types of CT scanners (1.39 mm ± 0.54 [moving patient table CT] vs 1.33 mm ± 0.52 [moving gantry CT]; P = .54) and was not significantly affected by the needle angulation and insertion depth. The accuracy for the porcine model was 9.09 mm ± 4.21. CONCLUSIONS: Robot-assisted needle insertion using the ANT-C robotic device was feasible and accurate for targeting multiple markers in a phantom model.

Heat Transfer Analysis of Nanofluid Flow in a Rotating System with Magnetic Field Using an Intelligent Strength Stochastic-Driven Approach.

This paper investigates the heat transfer of two-phase nanofluid flow between horizontal plates in a rotating system with a magnetic field and external forces. The basic continuity and momentum equations are considered to formulate the governing mathematical model of the problem. Furthermore, certain similarity transformations are used to reduce a governing system of non-linear partial differential equations (PDEs) into a non-linear system of ordinary differential equations. Moreover, an efficient stochastic technique based on feed-forward neural networks (FFNNs) with a back-propagated Levenberg-Marquardt (BLM) algorithm is developed to examine the effect of variations in various parameters on velocity, gravitational acceleration, temperature, and concentration profiles of the nanofluid. To validate the accuracy, efficiency, and computational complexity of the FFNN-BLM algorithm, different performance functions are defined based on mean absolute deviations (MAD), error in Nash-Sutcliffe efficiency (ENSE), and Theil's inequality coefficient (TIC). The approximate solutions achieved by the proposed technique are validated by comparing with the least square method (LSM), machine learning algorithms such as NARX-LM, and numerical solutions by the Runge-Kutta-Fehlberg method (RKFM). The results demonstrate that the mean percentage error in our solutions and values of ENSE, TIC, and MAD is almost zero, showing the design algorithm's robustness and correctness.

Cytotoxic Evaluation, Molecular Docking, and 2D-QSAR Studies of Dihydropyrimidinone Derivatives as Potential Anticancer Agents.

The diverse pharmacological role of dihydropyrimidinone scaffold has made it to be an interesting drug target. Because of the high incidence and mortality rate of breast cancer, there is a dire need of discovering new pharmacotherapeutic agents in managing this disease. A series of twenty-two derivatives of 6-(chloromethyl)-4-(4-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (3a-3k) and ethyl 6-(chloromethyl)-4-(2-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (4a-4k) synthesized in a previous study were evaluated for their anticancer potential against breast cancer cell line. Molecular docking studies were performed to analyze the binding mode and interaction pattern of these compounds against nine breast cancer target proteins. The in vitro cell proliferation assay was performed against the breast cancer cell line MCF-7. The structure activity relationship of these compounds was further studied using QSARINS. Among nine proteins, the docking analysis revealed efficient binding of compounds 4f, 4e, 3e, 4g, and 4h against all target proteins. The in vitro cytotoxic assay revealed significant anticancer activity of compound 4f having IC50 of 2.15 µM. The compounds 4e, 3e, 4g, and 4h also showed anticancer activities with IC50 of 2.401, 2.41, 2.47 and 2.33 µM, respectively. The standard tamoxifen showed IC50 1.88 µM. The 2D qualitative structure-activity relationship (QSAR) analysis was also carried out to identify potential breast cancer targets through QSARINS. The final QSAR equation revealed good predictivity and statistical validation R 2 and Q 2 values for the model obtained from QSARINS was 0.98 and 0.97, respectively. The active compounds showed very good anticancer activities, and the binding analysis has revealed stable hydrogen bonding of these compounds with the target proteins. Moreover, the QSAR analysis has predicted useful information on the structural requirement of these compounds as anticancer agents with the importance of topological and autocorrelated descriptors in effecting the cancer activities.