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Abstract Background Sentinel lymph node biopsy (SLNB) is widely used in patients who receive neoadjuvant chemotherapy (NAC). Still, axillary lymph node dissection (ALND) is recommended for patients with any axillary residual disease after NAC. The necessity of ALND in patients with minimal axillary disease is unclear. We aim to investigate regional recurrence rates in patients with limited axillary residual disease after NAC underwent SLNB + image-tailored axillary surgery and adjuvant radiotherapy (RT). Methods Patients with clinical stages were T1-3 and N1 at the time of diagnosis, clinically good or complete axillary response after NAC, and limited axillary residue (≤ 3 pathological lymph nodes) with favorable response to NAC in the final pathological examination were included in the study. All patients underwent SLNB + image-tailored axillary surgery. Peripheral lymphatic radiotherapy was applied, and no further surgery was performed in patients with compatible radiology and pathology results. Results Our study, which evaluated 139 patients with a median age of 47 years, found that the median number of excised lymph nodes was 4. Notably, 46% of patients had between 1 and 3 lymph nodes excised, while 45% had between 4 and 6. Only 9% of patients had ≥ 7 lymph nodes. 83(60%) of the patients underwent breast-conserving surgery (BCS), and 56(40%) underwent mastectomy. The study’s median follow-up period was 44 months. During this duration, one breast recurrence (0.7%), one supraclavicular recurrence (0.7%), and six systemic recurrences (4.3%) were observed. No axillary recurrence occurred within the follow-up period. Conclusions Patients presenting with pathological-suspicious ≤ 3 lymph nodes on imaging and showing a good response to NAC can be considered suitable candidates for SLNB + image-tailored axillary surgery, followed by adjuvant RT instead of ALND.

Conductive hearing loss caused by external or middle ear problems prevents the transmission of sound waves from the external auditory canal to the cochlea, and it is a common condition, especially in pediatric patients aged 1–5 years. The most common etiological factors are otitis media and cerumen during childhood. In some patients, external and middle ear functions and structures may be normal bilaterally despite the air-bone gap on the audiogram. This condition, which is often a missed diagnosis in children, is defined as a pseudo-conductive hearing loss (PCHL) caused by third window syndromes (TWSs) such as semicircular canal dehiscence, inner ear malformations with third window effect, and perilymphatic fistula. In this review of the literature, the authors emphasize the pitfalls of pediatric audio-vestibular evaluation on TWSs as well as the key aspects of this evaluation for the differential diagnosis of PCHL brought on by TWSs. This literature review will provide audiologists and otologists with early diagnostic guidance for TWSs in pediatric patients.

This study develops and evaluates models to estimate annual average daily traffic (AADT) at non-coverage or out-of-network locations. The non-coverage locations are those where counts are performed very infrequently, but an up-to-date and accurate estimate is needed by state departments of transportation. Two types of models are developed, one is that simply uses the nearby known AADT to provide an estimate, the other is that requires roadway features (e.g., type of median, presence of left-turn lane). The advantage of the former type is that no additional data collection is needed, thereby saving time and money for state highway agencies. A natural question that this study seeks to answer is: can this type of model provide equally as good or better estimates than the latter type? The models developed belonging to the first type include hybrid-kriging and Gaussian process regression GPR model (GPR-no-feature), and the models developed belonging to the second type include point-based model, ordinary regression model, quantile regression model, and GPR model (GPR-with-features). The performance of these models is compared against one another using South Carolina data from 2019 to 2021. The results indicate that the GPR-with-features model yields the lowest root mean squared error (RMSE) and lowest mean absolute percentage error (MAPE). It outperforms the hybrid-kriging model by 6.45% in RMSE, GPR without features model by 4.25%, point-based model by 4.69%, regular regression model by 11.35%, and quantile regression model by 4.25%. Similarly, the GPR-with-features model outperforms the hybrid-kriging model by 25.21% in MAPE, GPR without features model by 17.81%, point-based model by 22.26%, regular regression model by 26.36%, and quantile regression model by 21.07%.

The Covid-19 pandemic has significantly impacted consumer behavior and commerce, prompting a shift towards online goods and services. The surge in demand has led to inefficiencies and disruptions, especially in the last-mile delivery (LMD) process. Because of the LMD, the final stage of the supply chain, plays a crucial role in transporting goods from businesses to consumers, challenges such as the cost inefficiencies of direct home delivery have underscored the need for innovative solutions. In this study, the collection delivery points (CDPs) approach was adopted instead of direct home delivery. It focuses on addressing these challenges by adopting service points as dynamic CDPs and handling the problem as a dynamic location routing problem (DLRP). Two solutions approaches are proposed, to select candidate depots strategically and determine efficient route configurations, to aim to minimize travel distance. One of them is a two-phased hierarchical method that starts with clustering and continues with an Ant Colony Optimization (ACO) based-hybrid algorithm, and the other one is based solely on an ACO-based hybrid algorithm. The performance of these approaches is evaluated on modified benchmark instances from the literature. It has been observed that the ACO based-hybrid algorithm is more successful in terms of total travel distance, and if an evaluation is made in terms of the number of routes, it is recommended that the results of the two-phased hierarchical method should also be considered. Furthermore, a real word case study was conducted with the proposed methods and the results were compared from different perspectives. The results corroborate the findings regarding benchmark instances, thereby providing additional validation to the results obtained.

Abstract Breast-conserving surgery (BCS) followed by radiotherapy is preferred for early-stage breast cancer because its survival rate is equivalent to that of mastectomy. Achieving negative surgical margins in BCS is crucial to minimize the risk of recurrence. Intraoperative ultrasound (IOUS) enhances surgical accuracy, but its efficacy is operator dependent. This study aimed to compare the success of achieving negative margins using IOUS between an experienced breast surgeon and general surgical residents and to evaluate the learning curve for the residents. A prospective study involving 96 patients with BCS who underwent IOUS guidance was conducted. Both the breast surgeon and residents assessed the surgical margins using IOUS, with the breast surgeon making the final margin adequacy decision. Permanent histopathological analysis was used to confirm the status of the margins and was considered the gold standard for comparison. The breast surgeon accurately assessed the margin status in all 96 cases (100% accuracy), with 93 negative and three positive margins. All of these were ductal carcinomas in situ. Initially, the residents demonstrated low accuracy rates in predicting margin positivity using intraoperative ultrasonography. However, the learning curves of the three residents demonstrated that, with an average 12th case onwards, a significant improvement in the cumulative accuracy rates was observed, which reached the level of the breast surgeon. IOUS is an effective tool for accurately predicting the margin status in BCS, with an acceptable learning curve for novice surgeons. Training and experience are pivotal for optimizing surgical outcomes. These findings support the integration of IOUS training into surgical education programs to enhance proficiency and improve patient outcomes.

Abstract Background This study aimed to determine the effect of neoadjuvant chemotherapy (NACT) on the complex surgical procedures required in addition to staging surgery for the need to achieve a residual tumor 1 cm or less in a population of stage IIIC–IV epithelial ovarian cancer patients. Methods Patients were referred for NACT if preoperative imaging and/or intraoperative evaluation confirmed that it was not possible to achieve a residual tumor size of 1 cm or less with cytoreductive surgery or if the patient had a poor performance status and a high American Society of Anesthesiologists (ASA) score. Surgical complexity was defined as complex or non-complex. Results One hundred and twenty-six patients with stage IIIC–IV ovarian cancer were included in the study. Primary cytoreductive surgery was performed in 67 patients, and interval cytoreductive surgery was performed in 59 patients after NACT. At least one complex surgery was performed in 74.6% of the patients in the primary cytoreductive surgery group and in 61% of the patients in the NACT group, with no statistically significant difference between the groups. However, the NACT group showed significantly decreased rates of low-rectal resection, diaphragmatic peritoneal stripping, and peritonectomy. Conclusions The analyses showed no reduction in the requirement for at least one complex surgical procedure in the group of patients who underwent NACT. Nevertheless, this group exhibited a significant decrease in low-rectal resection, diaphragmatic peritoneal stripping, and peritonectomy due to their effectiveness in reducing peritoneal disease.

Abstract The transmembrane death receptor Fas transduces apoptotic signals upon binding its ligand, FasL. Although Fas is highly expressed in cancer cells, insufficient cell surface Fas expression desensitizes cancer cells to Fas-induced apoptosis. Here, we show that the increase in Fas microaggregate formation on the plasma membrane in response to the inhibition of endocytosis sensitizes cancer cells to Fas-induced apoptosis. We used a clinically accessible Rho-kinase inhibitor, fasudil, that reduces endocytosis dynamics by increasing plasma membrane tension. In combination with exogenous soluble FasL (sFasL), fasudil promoted cancer cell apoptosis, but this collaborative effect was substantially weaker in nonmalignant cells. The combination of sFasL and fasudil prevented glioblastoma cell growth in embryonic stem cell-derived brain organoids and induced tumor regression in a xenograft mouse model. Our results demonstrate that sFasL has strong potential for apoptosis-directed cancer therapy when Fas microaggregate formation is augmented by mechano-inhibition of endocytosis.

Abstract Background This study aimed to evaluate the outcomes of patients diagnosed with stage IB2/IIA2 cervical squamous cell carcinoma who underwent neoadjuvant chemotherapy (NACT) prior to radical hysterectomy compared to those who did not receive NACT before surgery. Materials and methods This is a multicenter study including data of 6 gynecological oncology departments. The study is approved from one of the institution’s local ethics committee. Patients were stratified into two cohorts based on the receipt of NACT preceding their surgical intervention. Clinico-pathological factors and progression-free survival were analyzed. Results Totally 87 patients were included. Lymphovascular space invasion (LVSI) was observed as 40% in the group receiving NACT, while it was 66.1% in the group not receiving NACT (p = 0.036). Deep stromal invasion (> 50%) was 56% in the group receiving NACT and 84.8% in the group not receiving NACT (p = 0.001). In the univariate analysis, application of NACT is statistically significant among the factors that would be associated with disease-free survival. Consequently, a multivariate analysis was conducted for progression-free survival, incorporating factors such as the depth of stromal invasion, the presence of LVSI, and the administration of NACT. Of these, only the administration of NACT emerged as an independent predictor associated with decreased progression-free survival. (RR:5.88; 95% CI: 1.63–21.25; p = 0.07). Conclusions NACT shouldn’t be used routinely in patients with stage IB2/IIA2 cervical cancer before radical surgery. Presented as oral presentation at National Congress of Gynaecological Oncology & National Congress of Cervical Pathologies and Colposcopy (2022/ TURKEY).

The present study is aimed to investigate the effect of empathy, conscience, self-control, kindness and self-efficacy on sportsmanship behaviors, since there is no study that investigated the effects of self-efficacy and moral intelligence on sportsmanship behaviors. For this purpose, the study was carried out according to the relational screening design, which is one of the quantitative research designs. 397 students studying at the faculty of sports sciences participated in the research. "Yakut-Moral Intelligence Scale", "Athlete Self Efficacy Scale" and "Investigation of Sportsmanship Behaviors Scale" were used to carry out the study. Statistical analyzes were made in SPSS 25 program and Pearson correlation and multiple linear regression analysis were used. As a result of the analysis, sportsmanship has been determined that it has a significant relationship with empathy, conscience, self-control, kindness and self-efficacy. As another result of the study, while self-efficacy alone explains 13.4% of sportsmanship, self-efficacy, empathy, self-control and kindness together explain 48% of sportsmanship. It was determined that the sub-dimension of conscience did not have a significant effect on sportsmanship. According to these results, in displaying sportsmanship behaviors; Having a high level of self-efficacy, ability to empathize, power to control behaviors and kindness have an important and powerful effect.

Decarbonization of the world is greatly contributed to by the recent technological advancements that have fostered the development of electric vehicles (EVs). The EVs relieve transportation dependence on natural fossil fuels as an energy source. More than 50 % of the petroleum products produced worldwide are estimated to be used in the transportation sector, accounting for more than 90 % of all transportation energy sources. Consequently, studies estimate that the transportation sector produces about 22 % of global carbon dioxide emissions, posing significant environmental issues. Thus, using EVs, particularly on road transport, is expected to reduce environmental pollution. To accelerate EV development and deployment, governments worldwide invest in EV development through various initiatives to make them more affordable. This research aims to investigate the changing needs of EV users to establish factors to be considered in the selection of charging demands using machine learning, using an extreme gradient boosting model. The model reached high accuracy, with an R2-Score of 0.964 to 1.000 across all predicted needs. The model performance is greatly affected by age, median income, education, and car ownership. High values of people with high income, high education, and age between 35–54 years show a positive contribution to the model’s performance, contrary to those with 65+, low income, and low education attainment. The outcomes of this research document factors that influence EV charging needs; therefore, it provides a basis for decision-makers and all stakeholders to decide where to locate EV charging stations for usability, efficiency, sustainability, and social welfare.

The recent advancements in Advanced Driver Assistance Systems (ADAS) have significantly contributed to road safety and driving comfort. An integral aspect of these systems is the detection of driver anomalies such as drowsiness, distraction, and impairment, which are crucial for preventing accidents. Building upon previous studies that utilized ensemble model learning (XGBoost) with deep learning models (ResNet50, DenseNet201, and InceptionV3) for anomaly detection, this study introduces a comprehensive feature importance analysis using the SHAP (SHapley Additive exPlanations) technique. The technique is implemented through explainable artificial intelligence (XAI). The primary objective is to unravel the complex decision-making process of the ensemble model, which has previously demonstrated near-perfect performance metrics in classifying driver behaviors using in-vehicle cameras. By applying SHAP, the study aims to identify and quantify the contribution of each feature – such as facial expressions, head position, yawning, and sleeping – in predicting driver states. This analysis offers insights into the model’s inner workings and guides the enhancement of feature engineering for more precise and reliable anomaly detection. The findings of this study are expected to impact the development of future ADAS technologies significantly. By pinpointing the most influential features and understanding their dynamics, a model can be optimized for various driving scenarios, ensuring that ADAS systems are robust, accurate, and tailored to real-world conditions. Ultimately, this study contributes to the overarching goal of enhancing road safety through technologically advanced, data-driven approaches.

Background: Despite all the advances in thyroid surgery, thyroidectomy remains an operation open to complications due to the recurrent laryngeal nerve (RLN) which the thyroid gland is closely surrounded by. This study aims to investigate the anatomical relationship of the RLN with the surrounding structures and define the relationships between these structures. Methods: In this prospective study, the relationship of the RLN with the surrounding anatomical structures, which consist of Berry’s ligament, inferior thyroid artery, tracheoesophageal groove, and the inferior constrictor muscle, and the presence of extralaryngeal branching were evaluated during thyroid surgery performed on 91 patients. Results: A total of 151 thyroid lobectomies were performed, which included 75 right and 76 left lobectomies. One patient presented a nonRLN on the right side, and of the remaining 150 thyroid lobectomy materials, 40 (26.7%) demonstrated a nerve passing anterior to, 93 (62%) passing inside, and 17 (11.3%) passing posterior to the tracheoesophageal groove. Evaluation of the relationship between the nerve and Berry’s ligament revealed that the nerve was located on the lateral side of Berry’s ligament in 114 lobectomies (76%). The nerve passed anterior to the inferior thyroid artery in 29 lobectomies (19.3%), between its branches in 17 (11.3%), and posterior to the artery in 104 (69.4%). The nerve was found to penetrate the crico-pharyngeal part of the inferior pharyngeal constrictor muscle in 8 lobectomies (5.3%), while it entered the larynx immediately under the muscle fibers in 142 (94.7%). The RLN demonstrated branching before entering the larynx in a total of 9 cases (6%). It was determined that the relationship of the nerve and the associated anatomical structures did not cause a significant difference in either side.

Detection and estimation of pothole dimensions is an essential step in road maintenance. Aging, heavy rainfall, traffic, and weak underlying layers may cause pavement potholes. Potholes can cause accidents when drivers lose control after hitting or swerving to avoid them, which may lead to injuries or fatal crashes. Also, potholes may result in property damages, such as flat tires, scrapes, dents, and leaks. Additionally, potholes are costly; for example, in the United States, potholes cost drivers about $3 Billion annually. Traditional ways of attending to potholes involve field surveys carried out by skilled personnel to determine their sizes for quantity and cost estimates. This process is expensive, prone to errors, subjectivity, unsafe, and time-consuming. Some authorities use sensor vehicles to carry out the surveys, a method that is accurate, safer, and faster than the traditional approach but much more expensive; therefore, not all authorities can afford them. To avoid these challenges, a modern, real-time, cost-effective approach is proposed to ensure the efficient and fast process of pothole maintenance. This paper presents a Deep Learning model trained using the You Only Look Once (YOLO) algorithm to capture potholes and estimate their dimensions and locations using only built-in vehicle technologies. The model attained 93.0 % precision, 91.6 % recall, 87.0 % F1-score, and 96.3 % mAP. A statistical analysis of the on-site test results indicates that the results are significant at a 5 % level, with a p-value of 0.037. This approach provides an economical and faster way of monitoring road surface conditions.

Fuzzing is a crucial technique for detecting software defects by dynamically generating and testing program inputs. This study introduces a framework designed to assess the application of Large Language Models (LLMs) to automate the generation of effective seed inputs for fuzzing, particularly in the Python programming environment where traditional approaches are less effective. Utilizing the Atheris fuzzing framework, we created over 38,000 seed inputs from LLMs targeted at 50 Python functions from widely-used libraries. Our findings underscore the critical role of LLM selection in seed effectiveness. In certain cases, seeds generated by LLMs rivaled or surpassed traditional fuzzing campaigns, with a corpus of fewer than 100 LLM-generated entries outperforming over 100,000 conventionally produced inputs. These seeds significantly improved code coverage and instruction count during fuzzing sessions, illustrating the efficacy of our framework in facilitating an automated, scalable approach to evaluating LLM effectiveness. The results, validated through linear regression analysis, demonstrate that selecting the appropriate LLM based on its training and capabilities is essential for optimizing fuzzing efficiency and facilitates the testing of future LLM versions.

In recent years, the incidence of cancers, inflammatory diseases, Alzheimer’s disease, glucose metabolism disorder and diabetes has increased alarmingly which demands more research into the discovery of new drug candidates to treat these human diseases. Main phytochemicals from Humulus lupulus L. (hops) have been demonstrated to have positive impacts on human health, and prenylated flavonoids are one of the major groups of bioactive phytochemicals found in this plant. Thus, this review summarizes the role of major prenylated components in hops in human diseases including cancer, inflammation and viral infections. In silico studies of prenylated bioactive compounds against various drug targets such as histone deactylases (HDACs), sirtuins (SIRTs), and acetylcholinesterase (AChE), and the molecular molecular interactions between protein and ligand have also been included. Furthermore, the structure-activity relationships (SAR) studies on these compounds are highlighted. This review concludes that the prenylated phytochemicals from H. lupulus L., including xanthohumol (XN), isoxanthohumol (IXN), 8-prenylnaringenin (8-PN) and 6-prenylnaringenin (6-PN), have promising roles in human health and may contribute to new drug discovery and development.

Facial skeletal asymmetries in Class III subjects seeking orthognathic surgery may complicate subsequent treatment.

The Coronavirus Disease 2019 (COVID-19) pandemic has considerably influenced school transportation in the United States (US), with conventional bus use declining and private vehicle use growing. It has increased traffic congestion, reliance on private autos, and detrimental air quality and sustainability effects. This study investigates the shift in transport mode preferences among parents during school reopening in 2021, focusing on elementary, middle, and high schools. An online questionnaire was distributed to parents in the U.S. The findings indicate a significant shift in parental transportation choices, with reduced reliance on school buses and increased automobile usage. Elementary school parents' bus percentage declined from 39.7% to 36.4% in Spring 2021, while middle and high school parents showed similar trends. School closures led to a shift to online learning platforms which affected academic achievement, with 15.9%, 15.5%, and 17.0% of students performing below average. In Spring 2021, 50.5% of students on the school bus encountered a delay of 0 to 15 min, while 32.4% suffered a wait of 15 to 30 min. In Fall 2021, 51.7% saw a 0 to 15-minute delay, while 31.2% suffered a 15-to-30-minute delay. Understanding the COVID-19 pandemic's effects on school travel patterns will allow policymakers and transportation planners to develop post-pandemic solutions prioritizing safety, justice, and environmental sustainability for similar events in the future.