Higher Priority Research Articles

Tennis Game Swing Prediction Model Based on ID3 Algorithm and Logistic Regression Model

Tennis, as one of the most popular sports worldwide, includes exciting matches between players. In some matches, score advantage can swing constantly from one side to another. These score swings may impact players’ performance and the result. To accurately predict the swings during a match between players, the article aims at building a score swing prediction model based on ID3 algorithm and logistic regression algorithm. The article will focus on analyzing tennis players’ behaviors and performance during a match. The preparation is to form a decision tree to evaluate diverse events’ impact on players’ mental strength, using ID3 algorithm that can analyze desired indicators with relevant data. By using the results from the decision tree evaluation, indicators with higher priority will be analyzed by the logistic regression model. The following Omnibus test and Hosmer-Lemeshow tests will estimate significance value and prediction accuracy. The results can show the prediction of scores swings in a match and indicate the factors that impact the match more. The study can provide tennis players with theoretical advice on their training activities, which may improve the ability of reversing the match and being consistent under pressure effectively.

Intuitive-K-prototypes: A mixed data clustering algorithm with intuitionistic distribution centroid

Data sets are usually mixed with numerical and categorical attributes in the real world. Data mining of mixed data makes a lot of sense. This paper proposes an Intuitive-K-prototypes clustering algorithm with improved prototype representation and attribute weights. The proposed algorithm defines intuitionistic distribution centroid for categorical attributes. In our approach, a heuristic search for initial prototypes is performed. Then, we combine the mean of numerical attributes and intuitionistic distribution centroid to represent the cluster prototype. In addition, intra-cluster complexity and inter-cluster similarity are used to adjust attribute weights, with higher priority given to those with lower complexity and similarity. The membership and non-membership distance are calculated using the intuitionistic distribution centroid. These distances are then combined parametrically to obtain the composite distance. The algorithm is judged for its clustering effectiveness on the real UCI data set, and the results show that the proposed algorithm outperforms the traditional clustering algorithm in most cases.

Fast Motion State Estimation Based on Point Cloud by Combing Deep Learning and Spatio-Temporal Constraints

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.

Use of the value-focused thinking methodology to understand health care waste management under the perspective of occupational and environmental health

The increasing generation of healthcare waste has greatly accelerated the concern about its potential negative environmental and public health effects. In this sense, the application of decision-making methods and techniques at a strategic level has become increasingly indispensable for the management of health units. This article aimed to understand the challenges in healthcare waste management (HCWM), the main mechanisms of action and values. The Value-Focused Thinking methodology was used to evidence important gaps and propose improvements in the context of decision on occupational safety and environmental protection, leading to the creation of alternatives not seen previously and guaranteeing the good ideas would be converted into actions. The structure was developed by semi-structured questionnaires, observation lists and interviews, conducted to expose the values of the main stakeholders (managers, health professionals, and cleaning professionals) of 10 public health units selected in Caruaru (Brazil). As a result, the investigation evidenced four fundamental goals for the decision context: improvement in work and environmental safety; improvement in HCWM practices; improvement in resource management; strengthening of the stakeholder communication network. From the analysis of the attributes associated with the fundamental goals, it was possible to identify actions that should be implemented with a higher priority and, in the short term, aiming at obtaining positive results, especially in the management practices and the safe management of health waste. The study resulted from an investigation that awakened the participants about aspects little questioned in everyday life. Implementing actions to segregate and recycle materials, with data monitoring and sharing, will increase the population's trust and the environment's and professionals' safety. The alternatives derived from the VFT method consider the perspectives of key stakeholders in HCWM, helping decision-makers and policymakers develop more assertive and grounded management strategies.

5G Communication Network and Federated Learning for College Accounting Informatization Teaching

At present, the social economy is entering the information age represented by computer, communication technology and network technology as the core, and the continuous development of modern information technology will certainly have a great impact on the teaching mode, content and methods of traditional accounting computerization. We aim to improve the existing higher vocational accounting teaching mode by building a multi-integrated teaching mode through federated learning based on 5G communication network as an environment for efficient information transfer. In addition, we develop a joint optimization strategy for priority-dependent task offloading, wireless bandwidth, and computational power in a distributed machine learning approach to ensure that more resources are allocated to users with higher priority while protecting user data privacy and reducing learning overhead. We have conducted extensive simulation experiments for both environments, and these simulation results demonstrate the effectiveness of our proposed solutions for different problems from different perspectives.

Microstate D as a Biomarker in Schizophrenia: Insights from Brain State Transitions.

Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease.

Fallo de medro: no siempre acierta, pero sí alerta

</jats:p> </jats:abstract> <publication_date media_type='online'> <month>09</month> <day>10</day> <year>2024</year> </publication_date> <doi_data> <doi>10.60147/0ee7e710</doi> <resource>https://pap.es/articulo/14232/fallo-de-medro-no-siempre-acierta-pero-si-alerta</resource> </doi_data> </journal_article> <!-- ============== --><journal_article publication_type='full_text'> <titles><title>Profile of the limping child </title><original_language_title>Perfil del paciente con claudicación de la marcha </original_language_title> </titles> <contributors> <person_name sequence='first' contributor_role='author'> <given_name>Verónica</given_name> <surname>Gimeno-Hernández </surname> </person_name><person_name sequence='additional' contributor_role='author'> <given_name>Álvaro </given_name> <surname>García</surname> </person_name><person_name sequence='additional' contributor_role='author'> <given_name>Marisol </given_name> <surname>Camacho</surname> </person_name></contributors> <jats:abstract xml:lang='en'> <jats:p>Introduction: lameness is a frequent reason for primary care and emergency department visits. Material and methods: observational, longitudinal, retrospective study in patients who presented with lameness to the emergency department of a tertiary care hospital between 2019-2022. Patients were included by systematic random sampling. The study variables were: age, sex, time from onset, associated symptoms, history of trauma, personal and family history, source of referral (provider vs self), examination findings, diagnostic tests and results, treatment, diagnosis in emergency department, final diagnosis, aetiological group and discharge destination. Results: 321 patients, 65.1% male, with a mean age of 5.5 ± 3.4 years. Sixty percent sought care within 24 hours of onset and 81.6% were self-referred. Diagnostic tests were ordered in 87.5% (X-rays in 55.7%). The most common aetiology was inflammatory (29%), chiefly on account of transient synovitis (24%). We found an association between provider referral and a higher priority in triage (p=0.03), the use of ultrasound (p=0.009) and referral to outpatient specialists (p=0.003). The mean age of patients with abnormal sonographic findings (5.65 years) was higher compared to patients with normal results (3.84 years). There were also mean differences in the ages of the different aetiological groups (p=0.000) and in the different diagnoses made in the emergency department (p=0.002). We found an association between the presence of previous symptoms and the aetiological group (p=0.000) and between the time elapsed from onset to the visit and the aetiological group (p=0.002), interconsultation with traumatology (p=0.000) and destination after discharge (p =0.000). Conclusions: most causes of lameness are banal, but a high index of suspicion should be maintained for less frequent alternative diagnosis that may have more serious repercussions, such as JIA.

Large language model triaging of simulated nephrology patient inbox messages.

Efficient triage of patient communications is crucial for timely medical attention and improved care. This study evaluates ChatGPT's accuracy in categorizing nephrology patient inbox messages, assessing its potential in outpatient settings. One hundred and fifty simulated patient inbox messages were created based on cases typically encountered in everyday practice at a nephrology outpatient clinic. These messages were triaged as non-urgent, urgent, and emergent by two nephrologists. The messages were then submitted to ChatGPT-4 for independent triage into the same categories. The inquiry process was performed twice with a two-week period in between. ChatGPT responses were graded as correct (agreement with physicians), overestimation (higher priority), or underestimation (lower priority). In the first trial, ChatGPT correctly triaged 140 (93%) messages, overestimated the priority of 4 messages (3%), and underestimated the priority of 6 messages (4%). In the second trial, it correctly triaged 140 (93%) messages, overestimated the priority of 9 (6%), and underestimated the priority of 1 (1%). The accuracy did not depend on the urgency level of the message (p = 0.19). The internal agreement of ChatGPT responses was 92% with an intra-rater Kappa score of 0.88. ChatGPT-4 demonstrated high accuracy in triaging nephrology patient messages, highlighting the potential for AI-driven triage systems to enhance operational efficiency and improve patient care in outpatient clinics.

An adaptive and multi-path greedy perimeter stateless routing protocol in flying ad hoc networks

In recent years, flying ad hoc networks (FANET), formed from unmanned aerial vehicles (UAVs), have absorbed the attention of academic and industrial research communities due to their many applications in military and civilian fields. FANETs benefit from unique features, including highly moving UAVs and dynamic topological structure. Therefore, most existing routing protocols, such as the greedy perimeter stateless routing (GPSR), are not compatible with the FANET environment and its specific features. To improve the performance of GPSR in FANET, it is important to address several challenges, namely the selection of the right period for broadcasting hello messages in the network, the selection of the right criteria for selecting the next-hop node, and the improvement of reliability in the data transfer process. In this paper, an adaptive and multi-path greedy perimeter stateless routing (AM-GPSR) protocol is suggested in FANETs. It includes two new strategies, namely adaptive hello strategy and multi-path greedy forwarding strategy. The adaptive hello strategy defines a special hello broadcast period for each UAV according to its speed and error between two estimated and actual positions. Furthermore, the greedy forwarding strategy carries out a filtering operation on candidate nodes and eliminates border UAVs and those that are far from the destination. Then, candidate UAVs are prioritized based on the time to reach the destination and buffer capacity, and UAVs with higher priorities are chosen to send data packets. Finally, AM-GPSR applies a greedy multi-path forwarding strategy to increase reliability in the data transmission process. Lastly, the simulation of AM-GPSR is done via the network simulator version 2 (NS2) to evaluate its performance. This evaluation process includes two different scenarios, i.e. change in the speed of UAVs and change in their communication range. In this process, AM-GPSR is compared with three other methods, namely the aerial greedy geographic routing (AGGR) protocol, the geolocation assisted aeronautical routing protocol (AeroRP), and GPSR. This comparison shows the successful performance of AM-GPSR in terms of delivery success rate, throughput, and delay. Although the control overhead of the proposed method is more than that of AGGR.

Exploring the Recycling Behavior and Climate Change Mitigation in Booming Countries: A Path to Cleaner Seas and Sustainable Futures- A Case of UAE

The expansion of the global population, technological progress, economic growth, and rapid urbanization have induced substantial shifts in lifestyle choices, reshaping consumption patterns. This research aimed to investigate the interrelation between recycling practices in rapidly developing nations, notably China, Brazil, Turkey, Vietnam, Nigeria, and the United Arab Emirates (UAE), while emphasizing the role of the UAE, particularly Abu Dhabi, in mitigating climate change and addressing marine pollution. Furthermore, the study ascertain the significance of recycling practices, specifically concerning plastic and e-waste in the UAE, in contributing to environmental sustainability and promoting cleaner oceans. This endeavor ultimately aimed for more sustainable and conscientious development pathways. The research methodology was founded on established theoretical and conceptual framework developed through an in-depth analysis of variables rooted in the theory of planned behavior. Following the successful collection of data on recycling behaviors related to plastic and e-waste in Abu Dhabi, the data was analysed using the Statistical Package for the Social Sciences (SPSS). The findings from the correlation analysis conducted showed complex correlations between recycling behavior and environmental factors. The positive connections found between “comparing recycling practice” and a favorable “future development pathway,” suggested active recycling involvement and good future perspectives. Similarly, positive correlation between “reducing marine pollution” and “environmental sustainability,” demonstrated a shared environmental conscience. However, a negative link was found between “mitigating climate change” and “reducing marine pollution”, “recycling behavior, showing that respondents with higher priority on mitigating climate change also not in favour to recycle and prioritize for reducing marine pollution.

Packaging for allocation

The allocation of indivisible objects is studied where individuals claim packages of them and a rule determines the number of each package to be allocated. There exists a rule satisfying feasibility, efficiency, and composition down (or composition up) if and only if the set of institutionally feasible packages is a hierarchy, i.e., for any pair of packages, either that they are disjoint or that one contains the other. Moreover, on a hierarchy, the unique rules satisfying the aforementioned properties are priority rules where a package has higher priority than another if it contains the other.

Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.

The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions. We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient's individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations. The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts. From a cohort of 214 patients and more than 16'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This "human baseline" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications of rules included e.g., adding or tightening certain conditions to make them less sensitive or changing the rule consequences: sometimes further monitoring the situation, applying certain test (such as a CRP test) or applying some simple pain-relieving measures was deemed sufficient, making a costly consultation with the physician unnecessary. We can thus conclude that it is possible to apply machine learning as an inspirational tool that can help human experts to formulate rules for an EWS. While humans seem to lack the ability to define such rules without such support, they are capable of modifying the rules to increase their precision and generalizability. Learning rules from dynamic ePRO datasets may be used to assist human experts in establishing an early warning system for cancer patients in outpatient settings.

Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer’s time availability in cloud manufacturing

ABSTRACT As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer’s capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

Incidence Rates and Predictors of Recurrent Long-Term Mental Sickness Absence Due to Common Mental Disorders.

Several predictors have been identified for mental sickness absence, but those for recurrences are not well-understood. This study assesses recurrence rates for long-term mental sickness absence (LTMSA) within subgroups of common mental disorders (CMDs) and identifies predictors of recurrent LTMSA. This historical prospective cohort study used routinely collected data from 16,310 employees obtained from a nationally operating Dutch occupational health service (ArboNed). Total follow-up duration was 23,334 person-years. Overall recurrence rates were assessed using Kaplan-Meier estimators. Recurrence rates within subgroups of CMDs were calculated using person-years. Univariable and multivariable Cox proportional hazards models were used to identify predictors. 15.6% of employees experienced a recurrent LTMSA episode within three years after fully returning to work after a previous LTMSA episode. Highest recurrence rates for LTMSA were observed after a previous LTMSA episode due to mood or anxiety disorders. Mood or anxiety disorders and shorter previous episode duration were predictors of recurrent LTMSA. No associations were found for age, gender, company size, full-time equivalent and job tenure. Employees should be monitored adequately after they fully returned to work after LTMSA. It is recommended to monitor high-risk employees (i.e. employees with mood or anxiety disorders and short LTMSA episode) more intensively, also beyond full return to work. Moreover, diagnosis of anxiety and depressive symptoms should be given a higher priority in occupational healthcare.

Comparison of different acupuncture therapies for dry eye disease: A systematic review and network meta-analysis

IntroductionDry eye disease (DED) has become increasingly prevalent in ophthalmology, significantly impacting patients' quality of life. Despite the widespread use of acupuncture therapies in treating DED, the most effective acupuncture treatment strategy remains uncertain. This study aimed to provide guidance in clinical practice for selecting the most effective acupuncture treatment strategy using the network meta-analysis (NMA) method. MethodsSeven English and Chinese databases were searched from their inception to October 15, 2023, to collect randomized controlled trials (RCTs) related to acupuncture therapies for treating DED. The risk of bias was assessed using the revised Cochrane risk of bias tool (ROB 2.0). Network meta-analysis was conducted using R 4.1.2 and Stata 17.0. The Confidence in Network Meta-Analysis (CINeMA) tool was used to evaluate the certainty of evidence. ResultsA total of 41 studies with 2931 participants were included. The network meta-analysis results showed that conventional acupuncture combined with thumbtack needle had the best efficacy in improving tear break-up time (TBUT) (MD = 2.95, 95 % CI [1.49, 4.39]) and Schirmer's test (MD = 3.34, 95 % CI [0.90, 5.79]). In terms of improving the corneal fluorescein staining score, conventional acupuncture combined with electroacupuncture demonstrated the best efficacy (SMD = -0.80, 95 % CI [-1.40, -0.18]). The confidence ratings of different treatment comparisons were judged as very low to low. Eight studies reported adverse effects, none of which were serious. ConclusionThe NMA results support the use of acupuncture for treating DED. Among the therapies, conventional acupuncture combined with thumbtack needle and conventional acupuncture combined with electroacupuncture have higher priority. However, before widespread adoption, more multicenter, randomized, double-blind, and placebo-controlled clinical trials are needed to confirm these findings.

Microbial mediated remediation of heavy metals toxicity: mechanisms and future prospects.

Heavy metal pollution has become a serious concern across the globe due to their persistent nature, higher toxicity, and recalcitrance. These toxic metals threaten the stability of the environment and the health of all living beings. Heavy metals also enter the human food chain by eating contaminated foods and cause toxic effects on human health. Thus, remediation of HMs polluted soils is mandatory and it needs to be addressed at higher priority. The use of microbes is considered as a promising approach to combat the adverse impacts of HMs. Microbes aided in the restoration of deteriorated environments to their natural condition, with long-term environmental effects. Microbial remediation prevents the leaching and mobilization of HMs and they also make the extraction of HMs simple. Therefore, in this context recent technological advancement allowed to use of bioremediation as an imperative approach to remediate polluted soils. Microbes use different mechanisms including bio-sorption, bioaccumulation, bioleaching, bio-transformation, bio-volatilization and bio-mineralization to mitigate toxic the effects of HMs. Thus, keeping in the view toxic HMs here in this review explores the role of bacteria, fungi and algae in bioremediation of polluted soils. This review also discusses the various approaches that can be used to improve the efficiency of microbes to remediate HMs polluted soils. It also highlights different research gaps that must be solved in future study programs to improve bioremediation efficency.

P-553 Embryo collection improves clinical pregnancy outcomes in next-generation sequencing(NGS)-based preimplantation genetic testing for aneuploidy (PGT-A) euploid embryo transfers

Abstract Study question Is there still a significant impact of embryo-related characteristics on pregnancy rates in NGS based PGT-A euploid embryo transfers for advanced-age patients? Summary answer In single frozen euploid embryo transfers, priority of embryo transfer and grade significantly influence clinical pregnancy rates in women over 35 years old with PGT-A. What is known already It is widely recognized that better embryo grades and a shorter duration of blastulation are associated with favorable reproductive outcomes. Recent meta-analyses have affirmed these findings even in the context of transfer with euploid embryos. However, the impact of prioritizing the highest-ranked embryo among multiple vitrified euploid embryos on pregnancy rates remains unclear. The lack of research in this area implies a gap in understanding whether selecting embryos for transfer among multiple viable euploid embryos could enhance reproductive success. Study design, size, duration Throughout the year 2022, a retrospective review was conducted on 428 cycles of single frozen-thawed euploid embryo transfers performed at a single institution, targeting patients aged 35 and older. The study aimed to explore potential differences in clinical pregnancy rates based on embryo grade, embryo freezing date, and embryo selection priority. Participants/materials, setting, methods Women ≥ 35 years old with single frozen euploid blastocyst transfer cycles was categorized into three groups: (a) with one blastocyst (1/1), (b) with ≥ two blastocysts having the top-priority(½ or higher), and (c) lower-priority blastocysts (lower than ½) to examine potential differences in clinical pregnancy rates among groups based on embryo characteristics, including Gardner grading and day of embryo freezing. Results highlight the impact of both selection criteria and freezing conditions on pregnancy outcomes. Main results and the role of chance This study analyzed a total of 428 frozen embryo transfer (FET) cycles. In the context of frozen-thawed single embryo transfer, the clinical pregnancy rate was 52.2% (128/245) when only one euploid embryo was frozen and subsequently thawed for transfer (a). For cases where two or more euploid embryos were frozen, and embryos with a higher priority (½ or higher) were selected for transfer (b), the clinical pregnancy rate significantly increased to 71.3% (62/87). Conversely, when embryos with a lower priority (lower than ½ in order) were chosen for transfer (c), the clinical pregnancy rate was lower at 38.5% (15/38), indicating a statistically significant difference between the groups (p = 0.001). Categorizing embryos based on Gardner grading, “good” embryos (>3BB) demonstrated a clinical pregnancy rate of 66.8% (141/211), while embryos with lower quality (3BB or below) showed a significantly lower clinical pregnancy rate of 40.6% (88/217) (p < 0.001). The analysis of clinical pregnancy rates based on the embryo freezing day revealed significant differences among embryos freezing on 5 days (71.9%, 133/185), 6 days (39.9%, 71/178), 7 days (25%, 2/8), and thawing PGT-A embryos (38.2%, 21/55) (P < 0.001). Limitations, reasons for caution One limitation of this study is its retrospective nature, and the potential confounding effect of the close association between embryo selection and embryo grading. Further analysis will be conducted to confirm correlations and address this potential confounding effect. Wider implications of the findings In single euploid frozen embryo transfers, selecting the highest-quality embryo through careful embryo selection remains relevant. Morphological indicators of embryos significantly impact early pregnancy outcomes, highlighting the continued importance of strategic embryo accumulation for enhanced procedural success. Trial registration number not applicable

A Descriptive Study on the Attitudes and Perceptual Barriers of Nursing Staff Towards Conducting Scientific Research

Nursing research has a tremendous influence on current and future up gradation of nursing profession. Mandatory nursing research courses are being taught in almost every nursing program. But unfortunately it is found that nursing lags behind in conducting scientific research in compare to other disciplines due to various factors. Aim: To assess the nursing faculty members' attitudes and perceived barriers towards conducting scientific research. Method: Study was conducted by using descriptive research design; self - structured questionnaire was used to collect data from 65 nursing faculties from different nursing colleges of Guwahati by using convenient sampling technique. Result: Out of 65 faculty members majority i. e. (96.9%) were female, (56.9%) were single, (50.8%) had completed B. sc Nursing/ Post Basic B. sc Nursing, (49.2%) were clinical instructor, (40%) had 1 - 3 years of working experience and (40%) had involved in both teaching and clinical duty. In assessing attitude majority i. e. (86.2%) answered that nurses with different degrees were not in need of knowledge based on the research as much as medical doctors, (93.8%) replied that nursing profession consists of practical work and does not have to include research, (95.4%) disagreed that nurses do not need of research in nursing to improve the nursing care as nurses can manage that by themselves, (96.9%) disagreed that participation in research work does not contribute to increased skills in nursing profession. Lack of confidence to perform the research activities, unawareness of using electronic data base, lack of knowledge about research methodology, no sufficient time to find research ROL, lack of statistical knowledge, see little benefits for self from research, other organizational goals are considered as higher priority rather than research activities, insufficient time on work place and overloaded with curriculum, no time for conducting research were found as barrier in conducting research. Conclusion: Nursing faculties were found having positive attitude towards conducting nursing research, but due to certain barrier they were unable to conduct it. Most of the faculties reported that they need proper guidance, statistical training, funds and mentors to conduct scientific research.

Sustainability of an Educational Program on Oral Care/Hygiene Provision by Healthcare Providers to Older Residents in Long-Term Care Institutions: A Follow-Up Study.

The importance of oral health in older adults, especially those in long-term care institutions (LTCIs), has been widely recognized. This study aimed to evaluate the sustainability of an oral health educational program (OHEP) for healthcare providers by measuring changes in their knowledge, attitudes, and practice (KAP) towards oral care provision 3 and 6 months after completing the OHEP. A pragmatic direct care nursing education trial with a control group was conducted to evaluate the sustainability of an OHEP by examining changes in KAP 3 and 6 months after the OHEP. The OHEP comprised both knowledge and skills related to oral care, whereas the control group received standard support in accordance with usual oral care practice. The study included 20 healthcare providers in the intervention group and 20 in the control group. At 6 months post-OHEP, a significant difference in knowledge was observed between the two groups, with the intervention group maintaining a positive effect (mean 13.90). Conversely, the control group showed a significant decline in knowledge (from mean 14.25 to 12.10). Both groups showed an improvement in attitudes regarding oral health, with the intervention group exhibiting better results 3 months post-OHEP. Intervention group participants rated oral care as a higher priority. An OHEP program for LTCI direct care staff provides enhanced knowledge and attitudes toward oral health care. Regular training in direct care and additional support may be needed to sustain optimal effects on oral care practice.

Dissecting through the Mind- The Art of History Taking in Psychiatry

History taking is an important aspect of teaching clinical skills in medicine during undergraduate and post-graduate courses. There is usually a uniformity as to how to take a reliable and informative history across various fields of medicine. Each subject, however has some specific questions pertaining to the subject which need to be addressed. History taking is given significantly much higher priority in the field of Psychiatry owing to lack of laboratory based diagnostic tools. Much of the diagnostic tools in Psychiatry are basically well structured, validated sets of questionnaires. Format of history taking remains somewhat similar in most of the literatures, but the individual skills to elicit history differs from person to person and no doubt it’s more of an art. This article aims to highlight the general format of history taking, the barriers in proper history taking, the situation specific skills, and overall art of extracting more and purposeful information from patients which can help reach the diagnosis. Keywords: History taking, Psychiatry, Skills