The rapid advancement of artificial intelligence has brought significant breakthroughs to various medical disciplines，This study aimed to compare perioperative factors and postoperative hip function recovery in primary total hip arthroplasty (THA)by evaluating the use of an artificial intelligence (AI) preoperative planning system versus traditional two-dimensional X-ray planning.

Many important information in medical research and clinical diagnosis are obtained from medical images. Among them, digital pathology images can provide detailed tissue structure and cellular information, which has become the gold standard for clinical tumor diagnosis. With the development of neural networks, computer-aided diagnosis presents the identification results of various cell nuclei to doctors, which facilitates the identification of cancerous regions. However, deep learning models require a large amount of annotated data. Pathology images are expensive and difficult to obtain, and insufficient annotation data can easily lead to biased results. In addition, when current models are evaluated on an unknown target domain, there are errors in the predicted boundaries. Based on this, this study proposes a feature alignment-based detail recognition strategy for pathology image segmentation (FASNet). It consists of a preprocessing model and a segmentation network (UNW). The UNW network performs instance normalization and categorical whitening of feature images by inserting semantics-aware normalization and semantics-aware whitening modules into the encoder and decoder, which achieves the compactness of features of the same class and the separation of features of different classes. The FASNet method can identify the feature detail information more efficiently, and thus differentiate between different classes of tissues effectively. The experimental results show that the FASNet method has a Dice Similarity Coefficient (DSC) value of 0.844. It achieves good performance even when faced with test data that does not match the distribution of the training data. Code: https://github.com/zlf010928/FASNet.git.

Pulmonary alveolar proteinosis (PAP) is characterized by the accumulation of surfactant material in alveoli. Few aPAP cases with a history of dust inhalation show both paves stone-like changes and micronodules in the chest CT scan. We present a 52-year-old male patient withsilicosis complicated with aPAP due to long-term dust inhalation during the construction of bridge piers columns. In this case report, chest CT of the patient displayed nonuniform ground-glass and patchy shadows in both lungs, paving stone-like changes, as well as diffuse distribution of high-density small nodular shadows, and the nodules tended to confluence.

Osteosarcoma (OS) is a primary malignant bone tumor in children and young adults because of its intertumoral heterogeneity and absence of molecular targets. This study aimed to elucidate the role of circular RNA in the pathogenesis of OS. Using bioinformatics analysis and OS clinical samples, we found that hsa_circ_0029634 formed during the splicing process of (circZMYM2) was highly expressed in OS tissues. Nuclear cap-binding protein subunit 2 regulated circZMYM2 bio-generation by binding to flanking sequences of transcripts. In addition, knockdown circZMYM2 inhibited cell growth and metastasis in OS cell lines and inhibited tumor growth . Mechanistically, circZMYM2 competitively bound to FXR1 to regulate the translation of NACA and ARPC1B. CircZMYM2 may be a crucial regulator of OS tumorigenesis and a potential diagnostic and therapeutic biomarker for OS patients.

Crystallization plays a crucial role as a separation and purification technique, particularly in the chemical and pharmaceutical industries. By adjusting process parameters, the productivity, product quality, and efficiency of downstream processes can be improved. In complex processes, model-based design becomes invaluable. Population Balance Models (PBMs) have successfully aided the chemical industry in achieving more effective production processes for decades. These models can utilize various input data sources to identify the dominant mechanisms and calibrate model parameters. While inline particle monitoring tools serve as excellent qualitative descriptors, challenges arise from experimentation and data interpretation, hindering their direct application in the kinetic parameter estimation of PBMs. In this study, we present a novel approach that utilizes information from inline particle monitoring tools for the kinetic parameter estimation of PBMs, bypassing the associated obstacles. Our pioneering approach relies on offline product size data and the correlation-based utilization of inline particle monitoring information. The paper compares this novel strategy with two parameter estimation techniques: the classical method using solute concentration and product size data and a somewhat naïve approach, which assumes that the inline particle monitoring data can directly be compared with the simulations. The prediction capabilities are evaluated through two in-silico case studies. The results indicate that the precision and predictive capability of the correlation-based technique are comparable to the classical approach, both for noisy data and for a system undergoing significant agglomeration and deagglomeration. The use of Pearson's correlation coefficient yields the best results in the novel cases. These findings in in-silico datasets provide a foundation and motivation for the practical application of this idea, unleashing the so-far hidden model development potential of such measurements.

This investigation examines the various factors that impact the decision-making process of small-scale farmers in utilizing agricultural information outlets within the designated research area. A total of 403 farmers was surveyed, and the obtained data were meticulously analyzed through the implementation of a multivariate probit model. The model's analysis reveals a significant and positive correlation between farm size, membership status, credit accessibility, market proximity, extension services, total income, and willingness to share information in relation to the farmers' preference for electronic information channels. Moreover, the projected Most Valuable Player for the selection of printed outlets is favorably influenced by the level of education, farm size, total income and membership status. Conversely, frequency of market visits and proximity to development centers all adversely impact the preference for electronic outlets. Furthermore, membership status exerts a beneficial influence on the choices pertaining to outlets centered around human interaction, while family size has a detrimental effect on such choices. In addition, membership status, total income, distance to market, and extension services all exert positive influences on the selection of outlets associated with organizational entities, whereas marital status and educational attainment levels exert negative influences. Consequently, if farmers are provided with access to multiple information outlets, they are able to selectively choose the most advantageous combination of information sources to optimize their agricultural outputs. It is therefore recommended that equitable access to information outlet choices be enhanced in potential production sites, as well as in the development of rural-urban infrastructure.

Recently, the importance of rapid testing nanosensor technologies for in-vitro diagnostics (IVD) and point-of-care testing (POCT) is being increasingly recognized. Owing to their high sensitivity and rapidity, nanosensor-based diagnostic devices are evolving into self-diagnostic tools that enable real-time in-situ analyses. These advances have become the focus of the public health control system, not only to prevent the spread of infectious diseases but also to enable the early detection of critical diseases through continuous health monitoring technologies. Current research on rapid diagnostic nanosensor technologies has focused on improving accuracy, sensitivity, rapid test systems, cost-effectiveness, and accessibility. In this review, we discuss the development of self-testing IVD technologies based on rapid diagnostic nanosensor platforms. The convergence of nanosensors and advanced materials can provide faster, more accurate, and more accessible IVD-based POCT solutions, representing an important advancement in healthcare. Here, we present nanosensor technologies for diagnosing biomarkers in in-vitro samples and improving diagnostic efficiency. Nanosensor-based self-diagnostic devices have the potential to be extended to personalized healthcare platforms, enabling the continuous monitoring of infectious diseases, cancer, and other serious issues. This review highlights the importance of research in this field to improve diagnostic efficiency.

Sustainable cities are crucial in establishing effective waste management systems and minimizing environmental pollution. For cities to be sustainable, different aspects need to be considered, such as technological development, clean energy usage, and energy efficiency. However, taking the most important actions is essential because of the very high cost that will arise, and this situation causes countries to have budget deficit problems. In other words, there is a significant need for a new study that makes a priority analysis with respect to the circular economy-based criteria for smart cities. Accordingly, this study aims to identify significant factors to improve sustainable cities using a novel decision-making model. First, essential determinants of the smart cities were evaluated with the decision-making trial and evaluation laboratory (DEMATEL) technique based on quantum spherical fuzzy sets (QASH) and facial expressions of the decision-makers. Second, smart investment choices for sustainable cities were ranked according to the technique for order preference by similarity to ideal solution (TOPSIS) approach. In addition, comparative ranking results were constructed together with sensitivity analysis. The ranking results of the extended VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are compared with the extended TOPSIS results and their sensitivity analysis results. The main contribution of this study is that appropriate priority strategies were determined by using an original methodology to have sustainable cities. A new methodology is developed in this study by the name of neuro decision-making. According to the comparative evaluation and sensitivity analysis, the findings are found as reliable and relevant. Resource efficiency is the most critical factor in improving sustainable cities. Constructing sustainable buildings is the most appropriate strategy for increasing smart cities. Necessary actions should be taken to minimize unconscious water and energy use. New technological developments need to be quickly adapted to businesses. In this way, it would be possible to perform the same work amount using less energy and water. For this purpose, it is important both to provide the necessary training and to emphasize the importance of these issues in television advertisements.

Near-infrared spectroscopy (NIRS) is a non-invasive and fast technology that has been increasingly used to analyse livestock diet quality. The objective of this study was to conduct a systematic review of the literature to examine the utilisation of NIRS technology for analysing livestock diet quality, with a focus on identifying trends, methodologies, and challenges in recent research. We conducted a systematic search of the literature on five electronic databases and retrieved 718 studies that have been published on the subject. Fifty-four studies were subsequently selected and investigated in depth. These studies were categorised into two groups, namely benchtop and portable, based on the types of NIRS devices utilised, with a majority employing the reflectance spectra mode. Our analysis found that standard normal variate (SNV), detrend (DT), and multiplicative scatter correction (MSC) are the most commonly used spectral data processing methods. The findings indicate that NIRS technology can provide accurate and reliable measurements of key livestock diet quality parameters such as crude protein, fibre, and moisture content. Additionally, we discuss the challenges associated with NIRS technology and provide recommendations for future research directions to further advance the use of NIRS technology in the livestock industry.

The incorporation of honeysuckle as a silage additive in alfalfa production has yielded promising results; however, the underlying microbial mechanisms during fermentation remain poorly understood. This study leveraged high-throughput sequencing and nutrient profiling to elucidate microbial population dynamics over 45 days of anaerobic fermentation within a vacuum-assisted co-culture system comprising varying fresh weight ratios of honeysuckle branches, , and alfalfa. The experimental design encompassed a -supplemented treatment (M group) and an unsupplemented control (R group), each with five honeysuckle percentages (5 %, 10 %, 15 %, 20 %, 25 %) and respective negative controls. Our findings reveal that the combined use of honeysuckle branches and significantly impacts microbial community composition and diversity during alfalfa silage fermentation. The Mantel test underscores 's absence-dependent correlation of bacterial communities with pH and lactic acid, whereas its presence redirects these correlations towards total nitrogen, NH -N, neutral detergent fiber, and crude protein as key bacterial population drivers. Fungal populations exhibited analogous trends. Moreover, the combined additives reshaped microbial populations during anaerobic fermentation, altering interaction networks and intensifying microbial interplay. Notably, introduction on a honeysuckle branch base diminished fungal core OTUs, thereby mitigating fungal contamination risks. This study also identified biomarkers unique to each treatment condition. Collectively, our research provides a molecular framework for employing honeysuckle branches and as innovative silage additives in alfalfa production, fostering the development of eco-friendly and hygienic feed and farming industries.

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Zinc performs different metabolic functions in human body and its deficiency is a major concern mainly in the third world countries. Fortification of Zn in grain crops may have the possibility to meet the challenge. The present study was designed utilizing plasma technology for paddy crop in two ways: (i) seed treatment with gliding arc argon-oxygen-ZnSO-HO discharge plasma jet in combination with nebulizing system for the fortification of Zn in the seeds, and (ii) plasma treated water (PTW) was sprayed to the plants cultivated from treated seeds and thereby investigated the growth and development of paddy plants defence system, nutritional composition and yield of paddy. Gliding arc argon-oxygen-ZnSO-HO discharge plasma jet was employed to treat paddy seeds with nebulizing system for the duration of 2-, 4-, 6-, and 8 min and the 4 min treatment condition provided the maximum 98 % germination among the conditions considered. Seedlings germinated from the 4 min treated seeds were resettled in the research area and PTW were applied two, four, and six times at the time of their growing period. The results reveal that the growth parameters, and the plants defence mechanisms were enhanced. Further, Zn concentration in the grains and the yield of paddy were increased by 59.07 %, 24.06 % and 38.94 %, 22.27 % respectively, compared to control and the plants grown from the treated seeds where no PTW foliar spray was applied. This investigation exposed that the fortification of Zn in seed by plasma processing and PTW spray to the plants which in turn improved seed germination, enzymatic activities, Zn accumulation in rice, and yield. It is fascinating that the paddy harvesting time was reduced more than two weeks compared to control.

Intramedullary spinal cord abscess (ISCA) is a rare and serious condition with high disability and mortality rates. is known for its aggressive and disseminated abscess formation. However, ISCA caused by has only been reported in two cases. Additionally, there have been no documented instances of invasive syndrome with abscesses in the brain, intramedullary spinal cord, renal subcapsular, and prostate simultaneously.

The COVID-19 pandemic preventive measures have successfully limited the spread of the infection but instituted changes in daily activities. This study examined the sociodemographic and health factors associated with lifestyle behavior changes among Omani adults during the COVID-19 pandemic. Sociodemographic factors investigated were age, gender, education, study (college versus non-college students), and marital status. A cross-sectional design was followed. We translated and tested a 20-item lifestyle behavior change during the COVID-19 pandemic questionnaire. A total of 515 responses were received with an average age of 25.7 years (SD ± 6.83). The participants' lifestyle behavior changes score mean was -2.75 (SD ± 9.08), indicating negative lifestyle change behavior. We found that lower lifestyle behavior changes scores were associated with younger age (p < .001), being single (p = .012), being a college student (p = .004), and having gained weight or were unsure about the weight change during the pandemic (p < .001). The linear regression model predicted a .19 increase in the lifestyle behavior change scores with each unit increase in age (B = .19, P = .02). Moreover, the model predicted more than a five-point decrease in the lifestyle behavior change scores among participants reporting a gain in weight or were unsure about the weight gain (B = -5.56, p < .001). These findings have significant implications for healthcare providers and policymakers. Actions to promote healthy lifestyle behaviors are essential to battle the increased risks of obesity after the pandemic, particularly among young adults. Additionally, mental and psychological wellbeing support during crises are vital for maintaining healthy lifestyle choices.

The increasing number of refugees in various regions of our country has led to socio-economic issues and challenges in the healthcare system. This study aims to investigate family physicians' perspectives in Turkey regarding refugees' social lives and healthcare services.

In 2022, 2034 incidents occurred at highway-rail grade crossings (HRGCs) in the United States, posing significant risks such as fatalities, injuries, and property damage. These incidents underscore the need for effective prevention and mitigation strategies. With over 212,000 public and private HRGCs nationwide, safety monitoring is challenging, as traditional inspections primarily rely on manual assessments. Obstructed sightlines at HRGCs further increase safety risks by limiting road users' ability to see approaching trains. Although previous studies have addressed behavioral and safety issues, the literature currently lacks quantitative data analysis of sightlines at HRGCs. This study aims to address this gap by utilizing remote sensing techniques to identify and quantify sightline visibility. We studied 12 HRGCs using geospatial analysis in ArcGIS Pro through Viewshed and Observer Points analysis, integrating United States Geological Survey (USGS) Light Detection and Ranging (LiDAR) data with United States Federal Railroad Administration (FRA) crossing reports. Our findings indicate that sightline issues in the case studies reviewed are primarily linked to traffic control devices, environmental factors, and geometric conditions. Additionally, the results suggest that USGS LiDAR data and geospatial analysis offer potentially rapid and cost-effective methods for identifying sightline safety issues at HRGCs.

The relationship between obesity and the conformational structure of hemoglobin (Hb) has not been extensively investigated. This study aims to elucidate the dielectric parameters that distinguish the Hb molecule under obese conditions and following treatment with goldenberry (GB) extract, compared to a control group. The dielectric parameters analyzed include the loss factor (D), relaxation time (τ), dielectric increment (Δε), relative permittivity (έ), dielectric loss (ε"), conductivity (σ), and Cole-Cole parameters (α), measured across a frequency range of 20 Hz to 3 MHz. Significant differences in dielectric parameters were observed between obese rats and those treated with GB extract. Obese rats exhibited higher dielectric values compared to the control group, while rats treated with low and high doses of GB extract showed marked changes in Hb conformational structure. This study highlights the potential of dielectric parameters as biophysical markers for detecting hemoglobin conformational changes. Furthermore, it suggests that dielectric behavior could serve as an early indicator for assessing the severity of obesity and its related complications.

Rainstorm floods have become a high-impact natural disaster and are expected to become more extreme shortly, seriously threatening human safety. Although the government issues timely and precise rainstorm flood warning information, citizens remain indifferent and engage in maladaptive behavior. Therefore, understanding the relationship between emergency information, psychological cognition, individual characteristics, and protective behavior is crucial for effective risk information communication and evacuation guidance.

Focused ultrasound-mediated opening of the blood-brain barrier offers a great opportunity to deliver therapeutics into hard-to-treat brain tumors such as glioblastoma multiforme or diffuse midline glioma. However, the potential of the technique to offer a time window for efficient nanomedicine delivery has not been thoroughly studied. Non-invasive and targeted delivery of large drug-loaded nanocarriers, such as liposomes, could offer a safe and scalable method of personalized therapy for the treatment of brain pathologies. Additionally, it is essential to monitor the safety and efficacy of such treatments, tracking drug delivery in real-time through quantitative medical imaging. In this study, liposomes were modified to have an MRI contrast agent (i.e., Gd) in both lipid membrane and core, while an infrared dye (i.e., CW800) was coupled to lipids introduced in the lipid bilayer for bimodal detection and treatment verification. Targeted delivery of 110 nm-in-diameter liposomes to the brain was quantified using 9.4-T MRI and near infrared fluorescence imaging. The spatiotemporal distribution of liposomes was assessed up to 4 h post treatment using T weighted MRI. MRI signal co-localized with NIRF signal from excised brains . Passive acoustic detection during treatments revealed a correlation between acoustic signal and MRI contrast, providing a scalable metric for assessing clinical treatment efficacy in real-time. In conclusion, therapeutic ultrasound exposure can enhance delivery of large trackable nanoparticles into the brain, while enabling real-time treatment monitoring and verification.

The coronavirus disease 2019 (COVID-19) pandemic has raised discussion over the connection between viral infections and the biology of cancer. Research has investigated the relationship between signaling pathways stimulated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that may be involved in the progression of cancer, resistance to chemotherapy, and metastasis. However, the exact cellular and molecular mechanisms of the effects of SARS-CoV-2 infection on cancer progression, chemo-resistance, metastasis, and recurrence have not been fully understood. Recently, studies indicate that SARS-CoV-2 might induce inflammatory responses and cytokine storm, which can affect cellular signaling pathways associated with the epithelial-mesenchymal transition (EMT). We address the possible involvement of reactive oxygen species (ROS) induced by SARS-CoV-2 infection in treatment resistance, metastatic recurrence, and the activation of EMT in solid tumors in this review. We emphasize the disturbance of mitochondria dysfunction, the overproduction of ROS in SARS-CoV-2-infected cells, and its consequences for the beginning of EMT. We also suggested possible processes associated with ROS influence on EMT, inflammatory signaling pathways, and viral interaction with mitochondria. Gaining knowledge about ROS's function in SARS CoV-2 condition, promoting EMT will help to develop effective strategies during therapy treatments by lowering drug resistance and metastatic recurrence in cancer patient.

Virtual Reality (VR) has proven to be a valuable tool for medical and nursing education. VR simulators are available at any time and from anywhere, and can be used with or without faculty supervision, which results in a significant optimization of time, space, and resources. In this paper we present a highly-configurable session designer for VR-based nursing education following the Standards for QUality Improvement Reporting Excellence: SQUIRE 2.0 and SQUIRE-EDU. Unlike existing platforms, we focus on letting educators quickly customize the training sessions in multiple aspects. This is achieved through the use of a visual editor, where educators can choose the environment and the elements in the session, and a node-based visual system for programming the behavior of the sessions. Thanks to end-user customization, educators can develop different variants of a session, adapt the sessions to new nursing protocols, incorporate new equipment or instruments, or accommodate custom environments. We have implemented the designer and tested it on various nursing training scenarios. The designer can be integrated into VR simulators to help educators save time in delivering highly customizable training.