Insufficient Data Research Articles

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.

Clinicopathological Characteristics and Outcomes of Colorectal Cancer With Heterogenous Staining of Mismatch Repair Protein.

Scant data are available on heterogenous staining of mismatch repair protein in colorectal cancer. This study aimed to improve insights into clinicopathologic features and prognosis of colorectal cancer harboring heterogenous mismatch repair protein staining. A single-center retrospective observational study. This study was conducted in a tertiary referral center in China between 2014 and 2018. Colorectal cancers with heterogenous staining of mismatch repair protein were included. Clinicopathologic and molecular features, and survival outcomes were analyzed. A total of 151 out of 6721 colorectal cancers (2.2%) exhibited heterogenous staining for at least one mismatch repair protein, with intraglandular heterogeneity being the most common pattern (89.4%). Heterogenous MLH1 staining was significantly associated with distant metastasis (p = 0.03), while heterogenous MSH2 staining was associated with left-sided (p = 0.03) and earlier pT stage tumors (p = 0.02). The rates of microsatellite instability-high, KRAS and BRAF mutation were 12.6%, 47.3% and 3.4%, respectively. Microsatellite instability-high was significantly associated with higher intraglandular MSH6 heterogeneity frequency (p < 0.001) and decreased MSH6 expression level (< 27.5%, p = 0.01). BRAF mutation was associated with the coexistence of intraglandular and clonal heterogeneity (p = 0.003) and decreased PMS2 expression level (p = 0.01). Multivariable analysis revealed that progression-free survival was significantly associated with tumor stage (p = 0.003), stroma fraction (p = 0.004), and heterogenous PMS2 staining (p = 0.02). Overall survival was linked to tumor stage (p = 0.006) and BRAF mutation (p = 0.01). The limitations of this study include the absence of testing for MLH1 promoter methylation and mismatch repair gene mutations, its retrospective design, and insufficient data related to direct comparison with deficient mismatch repair and proficient mismatch repair colorectal cancer. Heterogenous mismatch repair protein staining in colorectal cancer exhibits distinct associations with tumor location, stage, microsatellite instability, BRAF mutation and prognosis. It is recommended to report MSH6 heterogeneity as it may indicate microsatellite instability-high. See Video Abstract.

Cenobamate's Efficacy for Seizure Treatment in Tuberous Sclerosis Complex

BackgroundEpilepsy is prevalent, and seizure control is challenging in patients with tuberous sclerosis complex (TSC). Cenobamate (CBM) has proven efficacy in several studies; however, its benefit in the TSC population is not known. MethodsWe performed a retrospective review of patients with TSC who received adjunctive CBM for seizure treatments. We assessed treatment efficacy by comparing seizure frequencies three months before CBM (baseline) and those at 3-, 6-, 12-, and 18- month follow-ups. ResultsWe identified 70 patients with TSC receiving CBM and excluded 16 with insufficient data. Fifty-four patients aged 2 to 39 years, with an average baseline seizure of 66.1 ± 88.9 per month, were analyzed. Treatment retention rates at 3, 6, 12, and 18 months were 94.4%, 79.6%, 66.7%, 44.4%, and responder rates (proportions of patients who remained on treatment and had ≥50% seizure reduction) were 38.1%, 51.7%, 53.1%, and 59.1%, respectively. Seizure-free rates at these respective follow-ups were 7.1%, 13.8%, 6.3%, and 9.1%. For patients experiencing reduced seizures, the mean percentage of change ranged from 61.5% to 74.6%. Side effects were common (64.8%), particularly sedation (42.6%), behavioral disturbance (24.1%), and gastrointestinal disturbance (22.2%). ConclusionsMost patients in this study showed seizure reduction; however, the overall responder and seizure-free rates were lower than the literature, likely due to the unique underlying epileptogenesis in TSC and the challenges of tolerating CBM. The lower treatment retention rates signal areas for improvement in concurrent medication adjustment practices.

The efficacy of suprapatellar, parapatellar and infrapatellar intramedullary nailing in the treatment of tibial fractures: a systematic review and meta-analysis.

Tibial fractures are common and challenging orthopedic injuries that are commonly treated with intramedullary nailing techniques via suprapatellar (SP), parapatellar (PP), and infrapatellar (IP) approaches. This study aimed to provide a comprehensive comparative analysis of the efficacy of different treatment approaches based on clinical outcomes. We conducted a detailed search in PubMed, Cochrane Library, Embase, and Web of Science for clinical studies comparing suprapatellar, parapatellar, and infrapatellar approaches in intramedullary nailing of tibial fractures. Inclusion criteria included randomized controlled trials and retrospective cohort studies involving patients aged 18 and older, comparing outcomes of these surgical techniques. Exclusion criteria included studies with insufficient data, non-English publications, and those focusing on non-tibial fractures. A total of 15 studies involving 1396 patients were included in meta-analysis. Pooled results indicated that, compared to IP nailing, the SP approach significantly reduced fluoroscopy time (MD = -35.63, 95% CI -39.37 to -31.89, p < 0.001), operative time (MD = -10.72, 95% CI -17.30 to -4.15, p = 0.001), pain scores (SMD = -1.49, 95% CI -2.36 to -0.62, p < 0.001), and improved Lysholm scores (MD = 5.74, 95% CI 3.29 to 8.19, p < 0.001) and malalignment rate (RR = 0.24, 95% CI 0.08 to 0.68, p = 0.008). Quality of life assessments also indicated higher physical component scores for the SP group (MD = 6.68, 95% CI 5.19 to 8.17, p < 0.001). The SP approach provides significant intraoperative and postoperative benefits, reducing surgery time and improving patient outcomes in pain management and knee joint function. These findings support the SP approach as a preferred option for surgical treatment of tibial fractures.

Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments.

An integrated approach for mechanical fault diagnosis using maximum mean square discrepancy representation and CNN-based mixed information fusion

Bearings are an essential component of modern industry and cross-domain diagnosis of bearings holds significant importance. However, in practical applications, issues such as insufficient training data and differences between equipment present challenges. Transfer learning has become one of the effective methods to address these problems. This article proposes a fault diagnosis method that combines maximum mean square discrepancy (MMSD) measurement with the convolutional neural network (CNN) with mixed information (MIXCNN). MIXCNN enhances spatial position discrimination through deep convolution and achieves cross-channel information interaction using traditional convolution. The introduction of residual connections reduces information loss, while increasing network depth focuses on highly distinguishable features. MMSD constructs a metric that comprehensively reflects the mean and variance information of data samples in the reproducing kernel Hilbert space, thereby enhancing domain confusion. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with a maximum accuracy of 99.29%, providing reliable support for bearing fault diagnosis.

Modernizing Public Health Data Systems and Workforce Capacity: The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program.

The COVID-19 pandemic exposed governmental public health's outdated information technology and insufficient data science and informatics workforce capacity. The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program (PHIFP) is well positioned to strengthen public health data science and informatics workforce capacity. Established in 1996, PHIFP is a 2-year, full-time, on-the-job training program. PHIFP includes a didactic curriculum, applied learning through informatics projects completed at the assigned host site, short-term technical assistance projects, and a final capstone project. Fellows have learned from and bolstered host site informatics capacity through the development or enhancement of information systems, evaluations, data integration, data visualization, and analysis. Among recent graduates, 54% are employed at Centers for Disease Control and Prevention and 16% are employed at other public health organizations, including local health departments. Fellowships such as PHIFP, which recruit and train promising scientists in public health informatics, are important components of efforts to strengthen public health workforce capacity.

Barriers to Type 1 Diabetes Adherence in Adolescents.

Background: Adolescence is a particularly crucial period of physical, emotional, and social development and adaptation, rendering these formative years rather challenging for individuals with chronic conditions like type 1 diabetes (T1D). Despite rapid improvement in diabetes therapies, adolescents with T1D are characterized by poorer adherence to treatment regimens compared with other pediatric age groups. Insufficient adherence is strongly related to low diabetes control, increasing morbidity, and risk for premature mortality. This study aimed to provide a comprehensive overview of adolescents' most common barriers to T1D adherence, stressing the need for a deep and comprehensive understanding of these barriers. The complexity of these barriers is underscored by the diverse factors contributing to poor T1D adherence in adolescents. Methods: A narrative review was conducted, surveying four databases (PubMed, Scopus, EMBASE, and Web of Science) for full-text articles in the English language published up to June 2024. All studies related to barriers to T1D adherence in adolescents were considered. The literature was selected using selection and exclusion criteria and extracted and organized using Mendeley. Exclusion criteria included studies with insufficient data and non-peer-reviewed articles. This review revealed that adolescents face numerous obstacles to T1D adherence related to psychological factors, flux in family dynamics, perceived social pressures, therapy-related factors, transitioning responsibility, risk-taking behaviors, and pubertal changes. Conclusions: Navigating the adaptations to the different aspects of T1D, from treatment to complications and adolescents' personal growth, effectively requires a thorough understanding of the barriers of a treatment regimen that patients at this critical age face.

Rapidly Evaluating Species at Risk Using Endemic Plants of Kosrae, Federated States of Micronesia

The International Union for Conservation of Nature (IUCN)'s Red List of Threatened Species is regarded as the primary source of global extinction risk for species. Despite this importance, the IUCN has assessed less than 20% of the world′s estimated 400,000 flowering plants due to issues like insufficient data or a lack of experts. Thousands of conservation status assessments were generated recently in response to the call to action by the revised Target 2 of the Global Strategy for Plant Conservation (GSPC), “An assessment of the conservation status of all known plant species, as far as possible, to guide conservation action.” Accelerated extinction risk assessments are needed to identify species that are most at risk of extinction, which can then be thoroughly evaluated by the Red List assessment procedure. The land area in the Pacific Islands is significantly smaller than in other parts of the world. Plant species restricted to these islands may be identified as “At Risk” due to the limited land area of these islands, despite being widespread locally. Distribution and abundance data of single-island endemic plant taxa of Kosrae, Federated States of Micronesia, are analyzed and combined with digital elevation model files from this island, ArcGIS, and herbarium data, to calculate the percentage of land area that is available within a species′ elevational occurrence, a metric we have coined the Extent of Inhabitable Elevation of Island Occurrence (EIEIO). This metric can be used as a streamlined system to rapidly identify species that need plant conservation attention and allows for a swifter response to Target 2 of the GSPC.

EXAMINING CLINICAL STUDIES ABOUT CUPPING THERAPY IN TURKEY: A SYSTEMATIC REVIEW AND BIBLIOMETRIC ANALYSIS

There is insufficient quantitative data available on the characteristics of research conducted on cupping therapy. This insufficiency complicates our ability to achieve a more profound understanding of the subject. The primary aim of this work was to assess the outcome of clinical studies outputs in cupping therapy and discuss methodological gaps. Six databases PubMed, Cochrane Library, Medline, TUBITAK ULAKBIM DergiPark, Science Direct, and Google Scholar, were systematically searched from their inception to January 2024. Performance analysis was made in the study. Frequency and distributions were analyzed with SPSS 24. The software Wordclouds software was used to generate visualization. Within the scope of the research, 26 studies that met the selection criteria were accessed. It was determined that the majority of the studies (46.16%) were written in 2019-2021. The most used technique was 3 S (73.07%), and the most preferred point was du 14 point. While the top two variables are oxidative stress and heavy metal levels, the co-word analysis of keywords showed that "wet cupping therapy" was the largest single node. The findings from this bibliometric study provide the current status and productivity in clinical research of cupping therapy over the past ten years. It is seen that despite the challenges facing the field of cupping research, which include standardization, legal, and reporting quality, there has been an increase in publications to mitigate disease-related symptoms in clinical populations.

Exploring Large Language Models for Detecting Online Vaccine Reactions.

Social media offers a rich source of real-time health data, including potential vaccine reactions. However, extracting meaningful insights is challenging due to the noisy nature of social media content. This paper explores using large language models (LLMs) and prompt engineering to detect personal mentions of vaccine reactions. Different prompting strategies were evaluated on two LLM models (GPT-3.5 and GPT-4) using Reddit data focused on shingles (zoster) vaccines. Zero-shot and few-shot learning approaches with both standard and chain-of-thought prompts were compared. The findings demonstrate that GPT-based models with carefully crafted chain-of-thought prompts could identify the relevant social media posts. Few-shot learning helped GPT4 models to identify more of the marginal cases, although less precisely. The use of LLMs for classification with lightweight supervised pretrained language models (PLMs) found that PLMs outperform LLMs. However, a potential benefit in using LLMs to help identify records for training PLMs was revealed, especially to eliminate false negatives, and LLMs could be used as classifiers when insufficient data exists to train a PLM.

Swimtrans Net: a multimodal robotic system for swimming action recognition driven via Swin-Transformer.

Currently, using machine learning methods for precise analysis and improvement of swimming techniques holds significant research value and application prospects. The existing machine learning methods have improved the accuracy of action recognition to some extent. However, they still face several challenges such as insufficient data feature extraction, limited model generalization ability, and poor real-time performance. To address these issues, this paper proposes an innovative approach called Swimtrans Net: A multimodal robotic system for swimming action recognition driven via Swin-Transformer. By leveraging the powerful visual data feature extraction capabilities of Swin-Transformer, Swimtrans Net effectively extracts swimming image information. Additionally, to meet the requirements of multimodal tasks, we integrate the CLIP model into the system. Swin-Transformer serves as the image encoder for CLIP, and through fine-tuning the CLIP model, it becomes capable of understanding and interpreting swimming action data, learning relevant features and patterns associated with swimming. Finally, we introduce transfer learning for pre-training to reduce training time and lower computational resources, thereby providing real-time feedback to swimmers. Experimental results show that Swimtrans Net has achieved a 2.94% improvement over the current state-of-the-art methods in swimming motion analysis and prediction, making significant progress. This study introduces an innovative machine learning method that can help coaches and swimmers better understand and improve swimming techniques, ultimately improving swimming performance.

Opportunistic infections changed before and after SARS-CoV-2 infection in inflammatory bowel disease patients: a retrospective single-center study in China.

SARS-CoV-2 transmission has become a serious worldwide public health concern. However, there is currently insufficient data to determine whether SARS-CoV-2 infection would affect opportunistic infections in inflammatory bowel disease (IBD) patients. A retrospective study included 451 IBD patients (294 UC and 157 CD). The IBD patients were divided into two groups: before SARS-CoV-2 infection and after SARS-CoV-2 infection, and outcomes were measured for these groups. The primary outcome was the presence and distribution of opportunistic infections. The secondary outcomes included factors associated with opportunistic infections, based on which a nomogram prediction model was developed and validated. After SARS-CoV-2 infection, the proportion of IBD patients with opportunistic infections by Clostridium difficile (21.31% vs. 14.01%, p = 0.044) and Epstein-Barr virus (13.93% vs. 4.35%, p = 0.001) was significantly higher compared to that before. Conversely, the proportion of patients with hepatitis B virus (3.69% vs. 10.14%, p = 0.006) and herpes simplex virus type I (1.23% vs. 4.35%, p = 0.04) infections was significantly lower after the infection. Additionally, pre-SARS-CoV-2 infection factors associated with opportunistic infections in IBD include duration of illness, red blood cell count, the presence of comorbid chronic illnesses, and alcohol consumption, while post-SARS-CoV-2 infection, the primary risk factors involve corticosteroid use, red blood cell count, protein level, and high-sensitivity C-reactive protein. After the SARS-CoV-2 infection, there has been a shift in the occurrence of opportunistic infections among IBD patients. It might be attributed to the use of corticosteroids and also the strengthening of containment measures, heightened public health awareness, and widespread vaccination.

Nosocomial meningitis diagnostic test characteristics: a systematic review

The incidence of nosocomial meningitis, and utility of lumbar puncture, is unclear in hospitalized patients without preceding neurosurgery or head trauma. We planned for a systematic review and meta-analysis to evaluate accuracy of clinical features and diagnostic utility of lumbar puncture in nosocomial meningitis. We searched MEDLINE, MEDLINE In-Process/ePubs, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception until June 5, 2024. We included studies evaluating utility of clinical features, or lumbar puncture, to rule out nosocomial meningitis in patients without preceding neurosurgery or head trauma. We excluded studies examining community acquired meningitis, outbreaks, HIV positive individuals, and case reports. Outcomes included incidence, risk factors and diagnostic accuracy of clinical features for nosocomial meningitis, and lumbar puncture complications. Given few included studies and heterogeneity, we could only summarize incidence of nosocomial meningitis. Of 13,302 citations, we reviewed 197 manuscripts and included 6. There were 23 of 333 (6.9%, very low certainty) positive lumbar punctures among individuals who underwent lumbar puncture to rule out nosocomial meningitis. There were insufficient data to evaluate the diagnostic accuracy of lumbar puncture in nosocomial meningitis in patients without preceding neurosurgery or head trauma. Very low certainty evidence indicates the incidence of nosocomial meningitis is low in this population. Given complications and costs associated with lumbar puncture, future studies should evaluate its utility in nosocomial meningitis. In the meantime, it may be reasonable to reserve lumbar puncture to instances of high suspicion.

A Retrospective Study of the Profiles of Outpatients Attending the State Adolescent Friendly Health Resource Center, Andhra Pradesh, South India.

Adolescents compose a significantly large age group, and attention to their health needs is lacking due to insufficient data and policy implementation globally and in India. India, having 268 million adolescents, faces the world's largest adolescent population, which highlights the importance of addressing their health requirements. The aim of this study was to describe the morbidity profiles of adolescent patients attending the State Adolescent Friendly Health Resource Centre at a tertiary-level healthcare institution in South India. A retrospective secondary data analysis was conducted at the State Adolescent Friendly Health Resource Center from 2022 to 2023. Data on adolescent health were collected from this center's OPD using a structured questionnaire, which included information on demographics, immunization status, chief complaints, and dietary intake. Diagnosis coding was done using the International Classification of Diseases, Tenth Revision (ICD-10), with estimated energy and protein intake. The study analyzed 4,000 adolescent patients, with a nearly equal gender distribution across physical OPD and telemedicine services. Adolescent females between 15 and 16 years had more visits for consultation. Most patients were within the normal BMI range, and telemedicine was popular among females with normal BMI. Obesity was more common in males, while overweight was prevalent in females. Energy and protein intake varied across BMI categories, with obese individuals having the highest intake. The morbidity profiles revealed significant gender differences in healthcare utilization and disease patterns. Gender differences in adolescent morbidity and the underutilization of telemedicine highlight the need for tailored healthcare interventions and further research based on socioeconomic factors.

Intelligent full-stage stable fault diagnosis method for subsea production system

Subsea production system is a large slowly changing system. Through fault diagnosis, the state of it can be got in time, and this can ensure the smooth production of oil and gas. When the training data is insufficient, the performance of data driven methods is poor. Influenced by slow changes, the performance of model-based fault diagnosis methods may fluctuate. For long-term stable monitoring of subsea production system, an intelligent full-stage stable fault diagnosis method for subsea production system is proposed. A fault diagnosis framework with both digital and model drives is proposed. Model-based diagnostic model is used to achieve accurate diagnosis under insufficient data. Data driven model is trained continuously to achieve stable diagnosis. A dynamic digital twin model is proposed to accelerate the process of training. A case from the South China Sea is used to study the performance of this method. The results show that this method is stable over a long period.

The effects and benefits of creatine supplementation on brain health

Introduction: Creatine is a naturally occurring compound vital for energy metabolism, particularly in high-energy-demand tissues like muscles and the brain. Traditionally, research on creatine has focused on its ergogenic effects in sports, but emerging research suggests it may also positively impact brain health. Studies are exploring creatine monohydrate's role in improving cognitive function, mitigating neurodegenerative processes, and supporting mental health. This dual functionality highlights the compound's versatility as both a performance enhancer and a potential therapeutic agent for neurological health. By expanding the scope of creatine research beyond physical performance, we can better understand and utilize its full capabilities to enhance human health. This research review examines the growing evidence suggesting that creatine supplementation could significantly benefit brain health and the management of neurological diseases. Aim of the study: This review aims to synthesize current findings on creatine's cognitive benefits and its therapeutic potential for neurological disorders, critically analyzing preclinical and clinical studies to identify gaps and suggest future research directions.Materials and Methods: A comprehensive literature search was conducted using PubMed and Google Scholar databases, covering studies published between 2000 and 2024. Keywords such as creatine supplementation, brain health and neurodegeneration were used. Exclusion criteria included non-peer-reviewed articles, studies with insufficient data, and those not in English. Conclusions: This review highlights the potential benefits of creatine supplementation for cognitive and neurodegenerative diseases, noting its positive effects on memory, alleviating depression and anxiety, and offering neuroprotection. Future research should aim to standardize dosing, extend follow-up periods, and include larger, more diverse populations.

Comparative Analysis of the Performance and Study of the Effective Anchorage Length of Semi-Grouted and Fully-Grouted Sleeve Connection

Based on the insufficient data on bonding performance and effective anchorage length of sleeve grouting in assembled structure. Combining the existing studies, the sleeve grouting joint test for the static unidirectional tensile test was designed, and the influencing factors are reinforcement diameter and reinforcement anchorage length. Then, the failure mode, load-displacement relationship, energy consumption capacity and bearing capacity of the grouting sleeve connection are analysed, and the stress mechanism of the specimen in the one-way tensile state is expounded. This paper considers the actual damage state of the joint, according to the failure of the reinforcement outside the joint and the sleeve; referring to the reinforcement-concrete bond strength research theory, the effective anchorage length formula is proposed. When the steel bar is pulled out, the bond strength and bearing capacity mainly depend on the effective anchorage length. However, when the specimen breaks the steel bar outside the joint, it depends on the material performance of the steel bar itself. The research results of this paper can lay a theoretical foundation for the application of sleeve grouting joints.

General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning

Analyzing the unsteady aerodynamic performance of airfoils under dynamic stall using computational fluid dynamics (CFD) is computationally intensive. Although deep learning models can quickly predict aerodynamic parameters, their generalization capability on a small-scale dataset is often poor. This paper presents a novel deep transfer learning (TL) framework that combines model-based and instance-based transfer methods, termed synergistic instance-model TL. This framework facilitates rapid predictions of unsteady aerodynamic performance for various airfoils and pitch oscillations from the small-scale dataset. The framework integrates the accelerated training speed of model-based methods with the dynamic dataset expansion benefits of instance-based approaches. Initially, a pre-trained Wasserstein-deep convolutional generative adversarial network (W-DCGAN) is developed, combining a convolutional neural network with a generative adversarial network to predict aerodynamic hysteresis loops for the SC1095 airfoil in the source domain. The framework then fine-tunes the pre-trained model and incorporates weighted source domain dataset into the small-scale target domain dataset, producing the transferred model W-DCGAN-TL. This approach significantly reduces prediction inaccuracies compared to model-based and non-TL methods when applied to the small-scale dataset. The framework's flexibility allows the use of pre-trained models and datasets from related aerodynamic problems to address issues with insufficient data. Consequently, it is expected to reduce the dependency on extensive datasets, enhance design efficiency, and minimize resource requirements.

Vaccination Across the Lifespan: A Public Health Imperative

Vaccination is an essential public health intervention, preventing infectious diseases, saving millions of lives globally, and reducing the burden on the healthcare system. Currently, vaccines prevent between 3.5 and 5 million deaths annually from diseases like diphtheria, tetanus, pertussis, influenza, and measles.1 It not only protects individuals but also contributes to herd immunity, safeguarding entire communities. Maintaining high vaccination coverage across all age groups reduces the burden of preventable illnesses and safeguards public health. Childhood immunization is a cornerstone of preventive healthcare, providing protection against various life-threatening diseases during the most vulnerable years of life. Key vaccines include the DTP, MMR, IPV, Hib, and pneumococcal. 2 Global immunization coverage has stagnated since 2022, with 14.5 million children missing all DTP doses and 6.5 million not completing follow-up doses. There is an urgent need for innovative solutions to meet the Immunization Agenda 2030 targets of 90% vaccination coverage. 3 Vaccination has a pivotal role in women's healthcare, especially during pregnancy. The CDC recommends the inactivated flu and Tdap vaccines for protection of both mother and baby, with the flu vaccine best administered by the end of October and Tdap between 27 and 36 weeks of gestation. The HPV vaccine is recommended for girls aged 9–14 (two doses) and women over 14 (three doses) to protect against cervical cancer and genital warts, but it should not be given during pregnancy due to insufficient safety data.4 Regular vaccinations are crucial for adults aged 50–64, including COVID-19, influenza, shingles, and Tdap/Td. Hepatitis B is recommended for those up to 59 years old, and MMR for those born in 1957 or later. For those 65 and older, COVID-19, influenza, pneumococcal, and shingles vaccines are essential, with an RSV recommended. Healthcare providers should be consulted to determine additional vaccines for older adults, as regular vaccinations are important to prevent serious health issues due to immune decline and chronic conditions. 5 A comprehensive vaccination strategy is important for community health, encompassing children, women, adults, and older adults. A strong public health infrastructure that guarantees vaccine access and affordability should serve as the foundation for this strategy. In order to improve vaccination coverage, public awareness campaigns must be customized to address the concerns of all age groups. Children's immunization programs must be implemented in schools and coincide with routine physical examinations while women, particularly those who are of reproductive age, require special attention. Adult immunization programs must to prioritize occupational health, and older people need to remain updated. Considering immunization as a top public health priority, policy makers and healthcare professionals must work together to ensure continuous monitoring and evaluation of vaccination campaign’s coverage and effectiveness in order to spot gaps and launch prompt interventions.