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The advent of various new-age radiological modalities over the years has brought about paradigm shifts in diagnostics for clinicians all over the world. These advancements have refined medical practice immeasurably. Accurate diagnosis have, in turn, enabled surgeons to administer appropriate and timely surgical therapy to their patients. The timeline of path-breaking radiological inventions from X-rays to Ultrasonics, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and beyond, bears living testimony to the role that preoperative imaging plays in a surgeon’s daily life. The present review paper nostalgically remembers and acknowledges the pioneers of this remarkable journey. Additionally, it shares the authors’ experiences with classical radiological signs and appearances correlated with their corresponding surgical ‘first look’ pictures from some of the rare and interesting cases the authors have encountered over the years. The present cases exhibit diverse presentations, clinical signs, radiological findings and diagnosis. The crucial role played by radiology in clinicians’ daily lives, particularly in accurately diagnosing rare conditions, underscores the need to report these diverse cases.

In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used to identify crop diseases, as has crop classification-based image processing, they have failed to forecast accurate crop nutrients for various crops, as crop nutrients are numerical instead of visual. Neural networks represent an opportunity for the precision agriculture sector to more accurately forecast crop nutrition. Recent technological advancements in neural networks have begun to provide greater precision, with an array of opportunities in pattern recognition. Neural networks represent an opportunity to effectively solve numerical data problems. The aim of the current study is to estimate the right crop nutrients for the right crops based on the data collected using an artificial neural network. The crop data were collected from the MNIST dataset. To forecast the precise nutrients for the crops, ANN models were developed. The entire system was simulated in a MATLAB environment. The obtained results for forecasting accurate nutrients were 99.997%, 99.996%, and 99.997% for validation, training, and testing, respectively. Therefore, the proposed algorithm is suitable for forecasting accurate crop nutrients for the crops.

In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used to identify crop diseases, as has crop classification-based image processing, they have failed to forecast accurate crop nutrients for various crops, as crop nutrients are numerical instead of visual. Neural networks represent an opportunity for the precision agriculture sector to more accurately forecast crop nutrition. Recent technological advancements in neural networks have begun to provide greater precision, with an array of opportunities in pattern recognition. Neural networks represent an opportunity to effectively solve numerical data problems. The aim of the current study is to estimate the right crop nutrients for the right crops based on the data collected using an artificial neural network. The crop data were collected from the MNIST dataset. To forecast the precise nutrients for the crops, ANN models were developed. The entire system was simulated in a MATLAB environment. The obtained results for forecasting accurate nutrients were 99.997%, 99.996%, and 99.997% for validation, training, and testing, respectively. Therefore, the proposed algorithm is suitable for forecasting accurate crop nutrients for the crops. [ABSTRACT FROM AUTHOR]

Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithms in strengthening HIV testing worldwide. This systematic review aimed to evaluate how effectively ML algorithms can enhance the efficiency and accuracy of HIV testing interventions and to identify key outcomes, successes, gaps, opportunities, and limitations in their implementation. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A comprehensive literature search was conducted via PubMed, Google Scholar, Web of Science, Science Direct, Scopus, and Gale OneFile databases. Out of the 845 identified articles, 51 studies were eligible. More than 75% of the articles included in this review were conducted in the Americas and various parts of Sub-Saharan Africa, and a few were from Europe, Asia, and Australia. The most common algorithms applied were logistic regression, deep learning, support vector machine, random forest, extreme gradient booster, decision tree, and the least absolute shrinkage selection operator model. The findings demonstrate that ML techniques exhibit higher accuracy in predicting HIV risk/testing compared to traditional approaches. Machine learning models enhance early prediction of HIV transmission, facilitate viable testing strategies to improve the efficiency of testing services, and optimize resource allocation, ultimately leading to improved HIV testing. This review points to the positive impact of ML in enhancing early prediction of HIV spread, optimizing HIV testing approaches, improving efficiency, and eventually enhancing the accuracy of HIV diagnosis. We strongly recommend the integration of ML into HIV testing programs for efficient and accurate HIV testing. [ABSTRACT FROM AUTHOR]

This CME series reviews updated Delphi consensus surface anatomy terminology through the lens of common medical and procedural dermatology scenarios, helping to underscore high-yield points that can be readily integrated into clinical practice to support patient care. Part I of the series reviewed the current state of standardized surface anatomy, provided an illustrative review of consensus terminology, highlighted prominent landmarks that can aid in critical diagnoses, and related the importance of precise terminology to principles of medical management. Part II will utilize consensus terminology to heighten recognition of key landmarks in procedural dermatology to support optimal functional and aesthetic outcomes. [ABSTRACT FROM AUTHOR]

This continuing medical education series reviews updated Delphi consensus surface anatomy terminology through the lens of common medical and procedural dermatology scenarios, helping to underscore high-yield points that can be readily integrated into clinical practice to support patient care. Part I of this series will discuss the current state of surface anatomy terminology in dermatology, outline implications of precise and consistent terminology, provide an illustrative overview of high-yield consensus terminology, highlight prominent landmarks that can aid in critical diagnoses, and relate the importance of precise terminology to medical management. Part II will draw upon consensus terminology to inform the management of cutaneous malignancies and support optimal outcomes in dermatologic procedures. [ABSTRACT FROM AUTHOR]

Determination of equilibrium molecular structures is an essential ingredient in predicting spectroscopic parameters that help in identifying molecular carriers of microwave transitions. Here, the performance of two different ab initio composite approaches for obtaining equilibrium structures, "energy scheme" and "geometry scheme," is explored and compared to semi‐experimental equilibrium structures. This study is performed for a set of 11 molecules which includes diatomics, linear triatomics, and a few non‐linear molecules. The ab initio calculations were performed using three tiers of composite chemical recipes. The current results show that as the overall rigor of calculation is increased, the semi‐experimental and the ab initio numbers agree to within 0.0003 Å for all molecules in the test set. The composite approach based on correcting the potential energy surface (energy scheme) and the one based on correcting the geometry directly (geometry scheme) show excellent agreement with each other. This work represents a step toward development of efficient and highly accurate procedures for computing ab initio equilibrium structures. [ABSTRACT FROM AUTHOR]

Instance segmentation of foods is an important technology to ensure the food success rate of meal-assisting robotics. However, due to foods have strong intraclass variability, interclass similarity, and complex physical properties, which leads to more challenges in recognition, localization, and contour acquisition of foods. To address the above issues, this paper proposed a novel method for instance segmentation of foods. Specifically, in backbone network, deformable convolution was introduced to enhance the ability of YOLOv8 architecture to capture finer-grained spatial information, and efficient multiscale attention based on cross-spatial learning was introduced to improve sensitivity and expressiveness of multiscale inputs. In neck network, classical convolution and C2f modules were replaced by lightweight convolution GSConv and improved VoV-GSCSP aggregation module, respectively, to improve inference speed of models. We abbreviated it as the DEG-YOLOv8n-seg model. The proposed method was compared with baseline model and several state-of-the-art (SOTA) segmentation models on datasets, respectively. The results show that the DEG-YOLOv8n-seg model has higher accuracy, faster speed, and stronger robustness. Specifically, the DEG-YOLOv8n-seg model can achieve 84.6% Box_mAP@0.5 and 84.1% Mask_mAP@0.5 accuracy at 55.2 FPS and 11.1 GFLOPs. The importance of adopting data augmentation and the effectiveness of introducing deformable convolution, EMA, and VoV-GSCSP were verified by ablation experiments. Finally, the DEG-YOLOv8n-seg model was applied to experiments of food instance segmentation for meal-assisting robots. The results show that the DEG-YOLOv8n-seg can achieve better instance segmentation of foods. This work can promote the development of intelligent meal-assisting robotics technology and can provide theoretical foundations for other tasks of the computer vision field with some reference value. [ABSTRACT FROM AUTHOR]

Colloidal gold immunochromatographic test strip is a common detection method, which is widely used in disease diagnosis, biological toxin detection and other fields. The technique is based on the interaction of colloidal gold-labeled specific antigens (antibodies) with target molecules in the sample to be tested, forming visible test lines or signal changes on the test strip. The process includes sample pretreatment, labeling antibody preparation, and test strip assembly steps. Colloidal gold immunochromatographic test strip can be used for rapid detection of toxin residues in food, harmful substances in the environment and related indicators in clinical diagnosis. This technology has the characteristics of non-invasive, rapid, sensitive and accurate, so that it can quickly obtain results in clinical emergency detection and largescale screening. This paper summarizes the colloidal gold immunochromatographic test strip technology and its research progress in the detection of biological toxins, and analyzes the advantages and disadvantages of the current colloidal gold immunochromatographic test strip technology, and looks forward to the future development direction of the technology in the field of biological toxin detection [ABSTRACT FROM AUTHOR]

Abstract Background Precise pedicle screw placement of the subaxial cervical spine is difficult. Not every hospital is equipped with a guidance system that can provide effective help. Computed tomography (CT) scanning is almost a routine preoperative examination for cervical spine surgery in all hospitals. Appropriate measurement and analysis of the CT images could assist optimal cervical pedicle screw placement. The purpose of this study is to propose a new and universal method using computed tomography (CT) morphological parameters analysis to assist optimal cervical pedicle screw placement from C3 to C7. Methods A localization system with six parameters was designed based on preoperative CT reconstruction to guide subaxial cervical spine pedicle screw placement. The six parameters were distance from the starting point to the midline [D1], distance from the starting point to the lower edge of the inferior articular process [D2], transverse section angle [TSA], sagittal section angle [SSA], pedicle width [PW], and pedicle height [PH]. The six parameters were analyzed in 53 participants. Results Combining D1 and D2 could localize the entrance of the pedicle screw, and we concluded that D1 and TSA and D2 and SSA could be a new standard for determination of the transverse and sagittal orientation of the pedicle screw. The six parameters were closely related to the patient’s gender, height, and weight. PH and PW were linearly correlated and could guide selection of the appropriate pedicle screw. SSA was an independent parameter of the relevant vertebral body, and changes in SSA had nothing to do with the curvature or posture of the cervical spine. Conclusions Understanding and applying the six-parameter localization system are essential for achieving accurate and optimal pedicle screw placement in subaxial cervical spine, regardless of cervical sagittal alignment.

The article introduces a novel and developed a numerical technique that solves nonlinear time fractional differential equations. It combines finite difference and non-polynomial spline methods while using conformable descriptions of fractional derivatives. The strategy has proven to be useful to analyzing intricate datasets across various domains, including finance, science, and engineering. The applicability and validity of the approach are demonstrated through numerical examples. The study assessed the approach's stability using the Fourier method and found it to be unconditionally stable for a specific parameter range. The proposed method has been analyzed for convergence, and the analysis reveals that it exhibits a convergence order of six. The paper also includes graphs comparing the present solution to an analytical one. The method is tested with some examples of the model that have a wide range of applications in fields such as biology, ecology, and physics called the Burgers-Fisher equations and compared to previous approaches to demonstrate its effectiveness and applicability. The research evaluated the approach's accuracy and efficiency using the (L2 and L∞) norm errors. Also, the effects of time and fractional derivatives have been studied, and to the best of our knowledge, it has not been previously published in any academic or industry publication.

The roughness of the airfield runway is significant for the stability and safety of aircraft taking-off and landing. In view of the problem that the airfield runway roughness measuring instrument cannot accurately measure the large wavelength uneven information, an accurate and efficient method of the airfield pavement roughness measurement is proposed in this paper. Using data collector and data transfer unit control the robotic total station to track and capture the prism, then record the elevation between the prism and the robotic total station. At the same time, the data collector and data transfer unit also control the laser-ranging sensors to measure the elevation between the prism and the measuring points on the pavement. Knowing the elevation of the robotic total station, the elevation of the measuring points can be calculated using the data of the elevation. The curve of the runway surface profile can be measured. Considering the deviation caused by the roll or pitch angle of the vehicle while driving, a revised elevation method is proposed and deduced in this paper. It is verified by experiments that using the new device and the method mentioned in this paper is simple and efficient with high precision. [ABSTRACT FROM AUTHOR]

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice. [ABSTRACT FROM AUTHOR]

Estimating peak discharge in the catchment poses a challenge for hydrologists due to the potential overflow risks. The present study examined various methods, including Synthetic unit hydrograph, Melchior, and Rational, to determine the peak discharge value in the Lesti River Catchment Area. Accurate hydrological input data is essential for effectively estimating peak discharge and mitigating potential damage or failure. Optimized physical parameters using geographic information systems are critical in generating peak discharge values, emphasizing their importance in the estimation process. The mean absolute percentage error (MAPE) and mean average error (MAE) were used to determine the accurate method for estimating peak discharge. Analysis results showed varied peak discharge values across different methods, and it found that the MAPE values ranged from 15.15% to 7.48% and the MAE values ranged from 1.47% to 0.63%, respectively. The Nakayasu synthetic unit hydrograph obtained the minimum error measure value compared to other methods by 7.48% in MAPE and 0.63% in MAE. Therefore, the MAPE and MAE performance considered that the Nakayasu was closely approximating the observed peak discharge and a relatively accurate method for estimating peak discharge in the Lesti River catchment area. [ABSTRACT FROM AUTHOR]

An effective method for reliability-based design optimization (RBDO) problems taking uncertainties into account is the probabilistic feasible region (PFR) approach. The PFR approach is built around the fixed nature of the standard deviation in general RBDO problems. Therefore, the accuracy of the PFR approach will be affected when dealing with RBDO problems with varying standard deviation. To improve the accuracy of PFR method in solving the RBDO problems with varying standard deviation, a novel probabilistic feasible region strategy considering varying standard deviations (PFR-vstd) approach is suggested in this paper. In place of the initial probabilistic feasible region in standard normal space, a new probabilistic feasible region is established in original design space in this novel approach. The results of four applications demonstrate the high accuracy and sufficient efficiency of PFR-vstd method. The findings demonstrate that PFR-vstd method is capable of accurately resolving RBDO problems with varying standard deviation. [ABSTRACT FROM AUTHOR]

The phasor measurement unit (PMU) is an essential instrument for surveillance, control, and protection of power grids because it has the ability to provide critical time-synchronized real-time voltage, current, frequency, and phase data. PMUs are being deployed for real-time behaviour monitoring of power grids. The exactness and reliability of data obtained from PMUs can be ensured if the PMUs are calibrated from a traceable PMU calibrator. The facility of Phasor Measurement Unit Calibration System (PMU-CAL) has been established at the CSIR-National Physical Laboratory, New Delhi, in 2018 and is traceable to the national standards. By testing and calibrating PMUs according to the latest IEEE standard, PMU-CAL provides the best solution for PMU users, manufacturers, and energy suppliers. This paper presents the results of reference standard PMU calibrated for the measurement class carried out in the last five years from 2018 to 2022. The results show that the reference standard is highly stable and complies with the IEEE standard. With traceable measurements, once calibration is complete, all PMUs deployed in a country's grid will provide precise and reliable data that will help to increase efficiency, improve reliability, and enhance the quality of the power grid. [ABSTRACT FROM AUTHOR]

Background: Precise pedicle screw placement of the subaxial cervical spine is difficult. Not every hospital is equipped with a guidance system that can provide effective help. Computed tomography (CT) scanning is almost a routine preoperative examination for cervical spine surgery in all hospitals. Appropriate measurement and analysis of the CT images could assist optimal cervical pedicle screw placement. The purpose of this study is to propose a new and universal method using computed tomography (CT) morphological parameters analysis to assist optimal cervical pedicle screw placement from C3 to C7. Methods: A localization system with six parameters was designed based on preoperative CT reconstruction to guide subaxial cervical spine pedicle screw placement. The six parameters were distance from the starting point to the midline [D1], distance from the starting point to the lower edge of the inferior articular process [D2], transverse section angle [TSA], sagittal section angle [SSA], pedicle width [PW], and pedicle height [PH]. The six parameters were analyzed in 53 participants. Results: Combining D1 and D2 could localize the entrance of the pedicle screw, and we concluded that D1 and TSA and D2 and SSA could be a new standard for determination of the transverse and sagittal orientation of the pedicle screw. The six parameters were closely related to the patient's gender, height, and weight. PH and PW were linearly correlated and could guide selection of the appropriate pedicle screw. SSA was an independent parameter of the relevant vertebral body, and changes in SSA had nothing to do with the curvature or posture of the cervical spine. Conclusions: Understanding and applying the six-parameter localization system are essential for achieving accurate and optimal pedicle screw placement in subaxial cervical spine, regardless of cervical sagittal alignment. [ABSTRACT FROM AUTHOR]

An easy, accurate, and precise RP-UPLC was established and also validated for simultaneous quantitation of Bupivacaine and Meloxicam in the formulation as well as in pure form. An acceptable separation of Bupivacaine and Meloxicam was shown by the developed technique Bupivacaine and Meloxicam retention times were 1.359 min. and 3.687 min. correspondingly. From regression equations, LOQ and LOD were obtained and observed to be 0.2ppm and 0.6ppm - Bupivacaine, 0.06 and 0.0 I 8ppm - Meloxicam, respectively. Stability studies of the injectable dosage form under forced degradation conditions of acid, alkali, peroxide, thermal, hydrolysis, and ultraviolet rays showed that the technique was particular without interferences from any type of possible contaminations that might form over the shelf life. The newly established RP-UPLC technique was stability indicating, accurate, rapid, and sensitive, for active pharmaceutical ingredients and pharmaceutical dosage forms. [ABSTRACT FROM AUTHOR]

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability. [ABSTRACT FROM AUTHOR]

Commonly used numerical prediction models for PM 2.5 maps suffer from low accuracy and high computation cost, which cannot meet the requirements for fine-scale air pollution control. In this study, we propose a framework based on the spatiotemporal recurrent neural network (PredRNN) to efficiently generate accurate 3-h and 6-km PM 2.5 maps with a lead time of 5 days. In this framework, two PredRNN networks are initially utilized to forecast PM 2.5 concentration at ground monitoring sites and the spatial distribution of aerosol optical depth (AOD) by assimilating the output of numerical prediction model. Subsequently, the 3-h and 6-km PM 2.5 forecasted maps with a lead time of 5 days can be inferred by establishing the regression links between the forecasted results of PM 2.5 concentration at ground sites and AOD maps. We evaluate the proposed framework in the Beijing-Tianjin-Hebei urban agglomeration region during 2017–2020. Compared with the numerical prediction products of the Copernicus Atmosphere Monitoring Service, the proposed framework achieves higher accuracy, with R2 of 0.83 at the forecast base time and 0.70 at the fifth day. The spatial information richness is also enhanced by approximately 15.67% according to the information entropy metrics. Notably, the proposed framework only requires 1 min for forecasting 5-days PM 2.5 maps. These results demonstrate that our framework can efficiently generate accurate and fine PM 2.5 maps with a lead time of 5 days. • The framework can forecast more efficent and accurate fine PM 2.5 maps at a lead of 5 days than numerical prediction model. • Assimilating the numerical-model results can significantly improve the accuracy of the proposed forecast framework. • The forecasted PM 2.5 maps grasp more spatial details than the output of numerical prediction model. [ABSTRACT FROM AUTHOR]

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice.

William L Walter,1 Neri A Baker,2 Daniel Marsden-Jones,2 Ameneh Sadeghpour2 1Orthopaedics and Traumatic Surgery, University of Sydney, Sydney, New South Wales, Australia; 2Innovation Department, Navbit, Sydney, New South Wales, AustraliaCorrespondence: William L Walter, Tel +61 2 9160 6662, Email bill.walter@hipknee.com.auPurpose: The purposes of the present research were to assess the accuracy and usability of the inertial navigation system (INS).Materials and Methods: The accuracy of the device navigation subsystem was assessed using benchtop testing. The usability was assessed through simulated use with surgeons. These results were compared to recent cadaveric results for the same system.Results: The navigation subsystem had an overall mean absolute error of 1.21° and a maximum absolute error across all devices of 4.79°. The device was found to be usable and to add an estimated 7 minutes to surgery time.Conclusion: The INS uses a novel approach to provide the surgeon with accurate and fast acetabular cup inclination and anteversion angles during THA.Keywords: THA, navigation, imageless, accurate, inertial, intraoperative

Background. An x-ray request form (XRF) is a legal document that aims to communicate a physician's (medical practitioner's) request for radiological procedures to radiographers. Due to the need to conform to radiation protection regulations, the requestor has a responsibility to justify their requests and comply with the legal framework. This study aimed to assess the completeness of cervical spine and pelvis x-ray request forms (XRFs). Materials and method. A review of all plain film x-ray request forms for pelvis and cervical spine during the calendar month of September 2021 was performed to assess for completeness (adequacy) at two radiology departments in Windhoek by means of a retrospective cross-sectional design. A 15-item checklist was used to collect data, employing purposive sampling. The Statistical Software Package for Social Sciences (SPSS) version 27 was used to analyse and present data using descriptive statistics. Results. A total of 172 (110 pelvis and 62 cervical spines) XRFs were assessed. The majority were not adequately completed. Only 4.8% of C-spine and 1.8% of pelvis XRFs were adequately completed. Conclusion. The omissions of certain components on the XRFs are suggestive of ineffective communication between referring medical practitioners, radiologists, and radiographers, affecting the justification of procedures warranting a need for improvement in current referral practices. We recommend in-service training to sensitise medical practitioners and radiographers on the need for adequate completion of XRFs. [ABSTRACT FROM AUTHOR]

Neural probe plays an important role in neuroscience research and neurological diseases diagnosis. The flexible neural probes made by polymer emerging in recent years have been manifested more compliant and compatible with bio-tissues. However, the implantation of the flexible probe becomes another problem to be solved. In this letter, we propose a platinum black (Ptb) enhanced ultrathin flexible neural probe for accurate implantation. Benefiting from the anchoring structure on the flexible probe and the probe coupling with the shuttle by polyethylene glycol (PEG), reliable high-precision implantation has been demonstrated in agar model. Compared with traditional probe made with stiff shank, this flexible probe with anchoring structure is biocompatible and shows less immune reaction. In addition, the population spike (PS) was evoked successfully by biphasic stimulation in vivo. [ABSTRACT FROM AUTHOR]

Background: Blastocystis spp. is one of the most prevalent intestinal parasites with worldwide distribution. Various diagnostic methods with different sensitivities and specificities have been used to detect Blastocystis in clinical samples. The present study aims to develop and evaluate a LAMP assay to detect Blastocystis spp. in AIDS patients for the first time. Methods: In this cross-sectional study, 98 AIDS patients with an average CD4 + T lymphocyte count lower than 150 cells/mm3 participated in the study. The presence of Blastocystis spp. in the stool samples of AIDS patients was examined by parasitology (direct wet mount and concentration assays) and molecular (PCR and LAMP) methods. The 18 SSU rRNA genomic target was used to design the specific primers for the PCR and LAMP assays. The specificity of designed primers for the LAMP assay was evaluated using the sequencing of a conventional PCR product by the external LAMP primers. The data were analyzed using the SPSS software and chi-square test and Fischer's exact tests were used and Cohen's Kappa calculates the agreement of the molecular tests. Associations were tested using odd ratios (OR) and 95% confidence intervals (CI) after adjustments. Results: Out of 98 stool samples from patients with AIDS, 9 (9.18%), 13 (13.26%), and 15 (15.30%) samples were detected positive for Blastocystis spp. by parasitology, PCR, and LAMP techniques, respectively. PCR amplification and subsequent sequencing of the product sequences revealed that the obtained partial sequences were identical to the corresponding 18 SSU rRNA sequences reported in GenBank. The higher positivity rate for Blastocystis spp. among studied AIDS patients by LAMP technique compared to other diagnostic methods showed the higher potential and effectiveness of this relatively new described molecular assay for the detection of Blastocystis spp. in AIDS patients. Conclusion: The accurate and rapid detection of emerging intestinal protozoa such as Blastocystis is of clinical importance for better prevention and timely treatment of the disease, especially in immunocompromised patients. The results obtained for the first time showed that the sensitivity and accuracy of the LAMP technique in the diagnosis of Blastocystis spp. in AIDS patients is very high. [ABSTRACT FROM AUTHOR]

Background: Diagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors., Objective: We investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI., Methods: A quasi-experimental study was conducted at Dokkyo Medical University, Japan, with physicians allocated (1:1) to the intervention and control groups. A total of 20 clinical cases were created based on the medical histories recorded by an AI-driven automated medical history-taking system from actual patients who visited a community-based hospital in Japan. The participants reviewed the medical histories of 20 clinical cases generated by an AI-driven automated medical history-taking system with an AI-generated list of 10 differential diagnoses and provided 1 to 3 possible diagnoses. Physicians were asked whether the final diagnosis was in the AI-generated list of 10 differential diagnoses in the intervention group, which served as the trust calibration. We analyzed the diagnostic accuracy of physicians and the correctness of the trust calibration in the intervention group. We also investigated the relationship between the accuracy of the trust calibration and the diagnostic accuracy of physicians, and the physicians' confidence level regarding the use of AI., Results: Among the 20 physicians assigned to the intervention (n=10) and control (n=10) groups, the mean age was 30.9 (SD 3.9) years and 31.7 (SD 4.2) years, the proportion of men was 80% and 60%, and the mean postgraduate year was 5.8 (SD 2.9) and 7.2 (SD 4.6), respectively, with no significant differences. The physicians' diagnostic accuracy was 41.5% in the intervention group and 46% in the control group, with no significant difference (95% CI -0.75 to 2.55; P=.27). The overall accuracy of the trust calibration was only 61.5%, and despite correct calibration, the diagnostic accuracy was 54.5%. In the multivariate logistic regression model, the accuracy of the trust calibration was a significant contributor to the diagnostic accuracy of physicians (adjusted odds ratio 5.90, 95% CI 2.93-12.46; P<.001). The mean confidence level for AI was 72.5% in the intervention group and 45% in the control group, with no significant difference., Conclusions: Trust calibration did not significantly improve physicians' diagnostic accuracy when considering the differential diagnoses generated by reading medical histories and the possible differential diagnosis lists of an AI-driven automated medical history-taking system. As this was a formative study, the small sample size and suboptimal trust calibration methods may have contributed to the lack of significant differences. This study highlights the need for a larger sample size and the implementation of supportive measures of trust calibration., (© Tetsu Sakamoto, Yukinori Harada, Taro Shimizu. Originally published in JMIR Formative Research (https://formative.jmir.org).)

Background: Chronic edema management depends on measuring small volume changes over time. Therefore, a highly sensitive, accurate, and reliable technique is needed to objectively judge sequential measurements. Methods and Results: The prototype of the Peracutus Aqua Meth (PAM), a new volumetric measuring device, was used in an experimental study. Thirty-nine healthy test subjects were measured three times. The measuring principle of the PAM is based on obtaining a profile of cross-sectional areas along the length of an object or limb. Besides total arm volumes, the volume of any chosen segment can be determined. The utmost tip of the middle finger appeared to be an unreliable reference point. Instead, the concept of "hand median" was introduced to align and compare profiles of cross-sectional areas of sequential measurements. Using the hand median strongly improved the measuring accuracy, rendering standard deviation values of 0.4%-1.3% for (segmental) volume determination. Conclusions: Measurements with the PAM show that accurate local/segmental volume determination of a limb is possible. Standard deviations of less than 1.3% are easily obtained for cross-sectional area and volume determination.

Background: Self-expanding transcatheter valves (THV) seem superior to balloon-expanding valves in regard to the incidence of prosthesis–patient mismatch (PPM). Data on the occurrence of PPM with the ACURATE neo/neo2 system as a representative of self-expanding prostheses in very small annuli, even below the applicable instructions for use (IFU), are scarce. Methods: Data from 654 patients with severe native aortic stenosis treated with the smallest size ACURATE neo/neo2 valve (size S, 23 mm) at two German high-volume centers from 06/2012 to 12/2021 were evaluated. We compared clinical and hemodynamic outcomes among patients with implantation in adherence to the recommended sizing (on-label n = 529) and below the recommended sizing range (off-label n = 125) and identified predictors for PPM in the overall population. BMI-adjusted PPM was defined according to VARC-3 recommendations. Results: Post-procedure, the mean gradient (10.0 mmHg vs. 9.0 mmHg, p = 0.834) and the rate of paravalvular leakage (PVL) ≥ moderate (3.2% vs. 2.8%, p = 0.770) were similar between on-label and off-label implantations. The rate of moderate to severe PPM (24%) was comparably low in ACURATE neo/neo2 S, with a very low proportion of severe PPM whether implanted off- or on-label (4.9% vs. 3.8%, p = 0.552). Thirty-day all-cause mortality was higher among patients with off-label implantations (6.5% vs. 2.3%, p = 0.036). In the subgroup of these patients, no device-related deaths occurred, and cardiac causes did not differ (each 5). Besides small annulus area and high BMI, a multivariate analysis identified a greater cover index (OR 3.26), deep implantation (OR 2.25) and severe calcification (OR 2.07) as independent predictors of PPM. Conclusions: The ACURATE neo/neo2 S subgroup shows a convincing hemodynamic outcome according to low mean gradient even outside the previous IFUs without a relevant increase in the rate of PVL or PPM. In addition to known factors such as annulus area and BMI, potential predictors for PPM are severe annulus calcification and implantation depth. Nevertheless, the ACURATE neo/neo2 system seems to be a reliable option in patients with very small annuli. [ABSTRACT FROM AUTHOR]

The literature describes non-quantitative gas chromatographic methods for determining the relative content of various forms of long chain fatty acids in the biological material under study. Our task was to improve the quantitative and simultaneous gas chromatographic method for determining the absolute content of various forms of long-chain fatty acids in the biological material under study (plant and animal tissues and liquids). To do this, the studied biological material, a simple and complex internal standard is treated with various extracting mixtures. Various forms of long chain fatty acids are isolated from extracted lipids and their methyl esters are chromatographed. The results of gas chromatographic studies are calibrated. The improved method for low error allows quantitatively and simultaneously in absolute units to determine the content of total long chain fatty acids, long chain fatty acids of common lipids, esterified, non-esterified and anionic long chain fatty acids in the studied biological material. Thus, using the improved gas-chromatographic method at a lower cost of reagents and time, quantitatively and simultaneously in the biological material under study in absolute units we determine the content of various forms of long chain fatty acids. [ABSTRACT FROM AUTHOR]

Abstract Fascioliasis is a parasitic infection caused by Fasciola spp. in humans and animals. Despite significant advances in vaccination and new therapeutic agents, little attention has been paid to validate methods for the diagnosis of fascioliasis in animals. This study aimed to compare the loop‐mediated isothermal amplification (LAMP) technique with PCR assay for the diagnosis of F. hepatica in sheep. In this cross‐sectional study, 195 stool samples were collected from sheep for 3 months in Lorestan province, West of Iran. Specimens’ parasitological examination was performed by using the direct wet mount and formalin‐ether concentration method. After DNA extraction from the samples, molecular analysis was done using PCR and LAMP techniques based on the Fasciola ribosomal intergenic spacer (IGS) sequence. Of 195 specimens of sheep, 11 specimens were identified as F. hepatica‐positive infection by using microscopic, PCR and LAMP assays. Kappa agreement test results showed that there was a significant agreement between the results of microscopic examination diagnostic tests, PCR and LAMP (Kappa = 0.51–0.72 and p

Biografi, telah dikenal sejak lama bagi banyak orang sebagai suatu wadah yang menjadi informasi terkait dengan kisah hidup dan hasil pemikiran orang-orang terdahulu, terutama para kaum intelektual yang juga menyumbang banyak pemikiran dan konsep yang masih digunakan hingga saat ini. Oleh karena itu, biografi dan sejarah dari para intelektual ini memiliki relasi yang erat, yang mana dalam tulisan ini akan diteliti lebih lanjut oleh penulis. Penelitian yang digunakan dalam tulisan ini adalah penelitian yang sifatnya ialah kualitatif dengan sumber data sekunder dari studi pustaka. Dari hasil penelitian ini didapatkan hasil bahwa biografi dan sejarah intelektual memiliki kaitan yang erat, meskipun di satu sisi juga terdapat kelemahan diantara keduanya.

Background and Study Aim. It is stated that kinetic performance factors are important in the successful execution of accurate kick-penalties, thus, its offer excellent performance despite a substantial kinetic method change in their implementation. The aim of the study is to biomechanical analysis of accuracy penalties-kicking performance for Turkish soccer players. Material and Methods. The study group consisted 15 male students of Turkish Regional Amateur League players (Age: 21.08± 1.56 years old). Two video cameras placed at optical axes X&Y filmed penalty-kick performance of the subjects. we analysed the best three scores by video analysis Dartfish 9.0 software. Standard statistical methods were used for the calculation of mean±SD. the Statistical significance at p

There are well established dosimetry reference standards for broad beams; however, there are no reference standards that can be used for both broad and small fields. The variation of the equivalent square fields and field output factors in small static photon fields when using a synthetic diamond, an electron diode, and ionization chambers (pin point, semiflex, and liquid filled) was investigated over time. Data from this study were compared to the data from other hospitals in the country and standard data sets, i.e., the British Journal of Radiology Supplement No. 25 of 1996 (BJR25) and the Radiological Physics Centre (RPC) 2012 data. The results showed that reliance on one detector and one measurement session, could yield incorrect field output factors (FOFs) for small fields. At least one of the detectors should be a solid state type with published field output correction factors and at least three measurement sessions should be performed for each FOF data point. Comparing measured data with published datasets, like RPC, will assist in verifying data. BJR25 datasets should not be used for Sclin ≤4 cm.

There is a recent boom being witnessed in emerging areas like IoMT (Internet of Medical Things), Artificial Intelligence for healthcare, and disaster management. These novel research frontiers are critical in terms of hardware and cannot afford to compromise accuracy or reliability. Multiplier, being one of the most heavily used components, becomes crucial in these applications. If optimized, multipliers can impact the overall performance of the system. Thus, in this paper, an attempt has been made to determine the potential of accurate multipliers while meeting minimal hardware requirements. In this paper, we propose a novel Booth-Encoded Karatsuba multiplier and provide its comparison with a Booth-Encoded Wallace tree multiplier. These architectures have been developed using two types of Booth encoding: Radix-4 and Radix-8 for 16-bit, 32-bit and 64-bit multiplications. The algorithm is designed to be parameterizable to different bit widths, thereby offering higher flexibility. The proposed mul- tiplier offers advantage of enhanced performance with significant reduction in hardware while negligibly trad- ing off the Power Delay Product (PDP). It has been observed that the performance of the proposed architecture increases with increasing multiplier size due to significant reduction in hardware and slight increase in PDP. All the architectures have been implemented in Verilog HDL using Xilinx Vivado Design Suite.

Background: The release of ChatGPT (OpenAI) in November 2022 drastically reduced the barrier to using artificial intelligence by allowing a simple web-based text interface to a large language model (LLM). One use case where ChatGPT could be useful is in triaging patients at the site of a disaster using the Simple Triage and Rapid Treatment (START) protocol. However, LLMs experience several common errors including hallucinations (also called confabulations) and prompt dependency., Objective: This study addresses the research problem: "Can ChatGPT adequately triage simulated disaster patients using the START protocol?" by measuring three outcomes: repeatability, reproducibility, and accuracy., Methods: Nine prompts were developed by 5 disaster medicine physicians. A Python script queried ChatGPT Version 4 for each prompt combined with 391 validated simulated patient vignettes. Ten repetitions of each combination were performed for a total of 35,190 simulated triages. A reference standard START triage code for each simulated case was assigned by 2 disaster medicine specialists (JMF and MV), with a third specialist (LC) added if the first two did not agree. Results were evaluated using a gage repeatability and reproducibility study (gage R and R). Repeatability was defined as variation due to repeated use of the same prompt. Reproducibility was defined as variation due to the use of different prompts on the same patient vignette. Accuracy was defined as agreement with the reference standard., Results: Although 35,102 (99.7%) queries returned a valid START score, there was considerable variability. Repeatability (use of the same prompt repeatedly) was 14% of the overall variation. Reproducibility (use of different prompts) was 4.1% of the overall variation. The accuracy of ChatGPT for START was 63.9% with a 32.9% overtriage rate and a 3.1% undertriage rate. Accuracy varied by prompt with a maximum of 71.8% and a minimum of 46.7%., Conclusions: This study indicates that ChatGPT version 4 is insufficient to triage simulated disaster patients via the START protocol. It demonstrated suboptimal repeatability and reproducibility. The overall accuracy of triage was only 63.9%. Health care professionals are advised to exercise caution while using commercial LLMs for vital medical determinations, given that these tools may commonly produce inaccurate data, colloquially referred to as hallucinations or confabulations. Artificial intelligence-guided tools should undergo rigorous statistical evaluation-using methods such as gage R and R-before implementation into clinical settings., (©Jeffrey Micheal Franc, Attila Julius Hertelendy, Lenard Cheng, Ryan Hata, Manuela Verde. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.09.2024.)

Background: Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting picture is rated based on the depicted pain intensity. Analyzing pain drawings (PDs) typically involves measuring the size of the pain region. There are several studies focusing on assessing the clinical use of PDs, and now, with the introduction of digital PDs, the usability and reliability of these platforms need validation. Comparative studies between traditional and digital PDs have shown good agreement and reliability. The evolution of PD acquisition over the last 2 decades mirrors the commercialization of digital technologies. However, the pen-on-paper approach seems to be more accepted by patients, but there is currently no standardized method for scanning PDs., Objective: The objective of this study was to evaluate the accuracy of PD analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire PDs without losing important information., Methods: Two sets of PDs were generated: one with the addition of 216 colored circles and another composed of various red shapes distributed randomly on a frontal view body chart of an adult male. These drawings were then printed in color on A4 sheets, including QR codes at the corners in order to allow automatic alignment, and subsequently scanned using different devices and apps. The scanners used were flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer or scanner), smartphones with varying price ranges, and 6 virtual scanner apps. The acquisitions were made under normal light conditions by the same operator., Results: High-saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. In addition, a significant negative correlation was observed between the percentage of error and spot size (R=-0.237; P=.04). The proposed platform proved to be robust and reliable for acquiring paper PDs via a wide range of scanning devices., Conclusions: This study demonstrates that a web platform can accurately analyze PDs acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for PD acquisition without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent PD analysis in clinical and research settings., (© Corrado Cescon, Giuseppe Landolfi, Niko Bonomi, Marco Derboni, Vincenzo Giuffrida, Andrea Emilio Rizzoli, Paolo Maino, Eva Koetsier, Marco Barbero. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org).)

The multiattribute method (MAM) has emerged as a powerful tool for simultaneously screening multiple product quality attributes of therapeutic antibodies. One such potential critical quality attribute (CQA) is glycation, a common modification that can impact the heterogeneity, functional activity, and immunogenicity of therapeutic antibodies. However, current methods for monitoring glycation levels in MAM are rare and not sufficiently rapid and accurate. In this study, an improved mass spectrometry (MS)-based MAM was developed to simultaneously monitor glycation and other quality attributes including afucosylation. The method was evaluated using two therapeutic antibodies with different glycosylation site numbers. Treatment with IdeS, Endo F2, and dithiothreitol generated three distinct subunits, and the glycation results obtained were similar to those treated with PNGase F, which is routinely used to release glycans; the sample processing time was greatly reduced while providing additional quality attribute information. The MS-based MAM was also employed to assess the glycation progression following forced glycation in various buffer solutions. A significant increase in oxidation was observed when forced glycation was conducted in an ammonium bicarbonate buffer solution, and a total of 23 potential glycation sites and 4 significantly oxidized sites were identified. Notably, we found that ammonium bicarbonate was found to specifically stimulate oxidation, while glycation had a synergistic effect on oxidation. These findings establish this study as a novel methodology for achieving a technologically advanced platform and concept that enhances the efficacy of product development and quality control, characterized by its broad-spectrum, rapid, and accurate nature.