Improve Success Rates Research Articles

Network-Based Approaches for Drug Target Identification.

Drug target identification is the first step in drug development, and its importance is underscored by the fact that, even when using genetic evidence to improve success rates, only a small fraction of lead targets end up approved for use in the clinic. One of the reasons for this is the lack of in-depth understanding of the complexity of human diseases.In this review we argue that network-based approaches, which are able to capture relationships between relevant genes and proteins, and diverse data modalities have high potential for improving drug target identification and drug repurposing. We present the evolution of network-based methods that have been developed for this purpose and discuss the limitations of these approaches that are holding them back from making an impact in the clinic. We finish by presenting our recommendations for overcoming these limitations, for example, by leveraging emerging technologies such as artificial intelligence and knowledge graphs.

Clinical efficacy of tunnel flap combined with de-epithelialized connective tissue graft in treating gingival recession: a case-series study.

To evaluate the clinical efficacy and influencing factors of the tunnel flap (TUN) combined with de-epithelialized connective tissue graft (DCTG) in the treatment of gingival recession (GR) from patient- and site-specific perspectives. Patients treated with the TUN + DCTG technique for GR between August 2020 and December 2023 were included. Baseline and follow-up periodontal measurements were recorded, including gingival recession depth (GRD), keratinized gingival width (KGW), and clinical attachment loss (CAL). Paired sample t-test was performed to assessthe clinical efficacy. Moreover, primary outcome metrics, such as complete root coverage rate (CRC), mean root coverage rate (MRC), root coverage esthetic score (RES), and patient satisfaction, were calculated. Patient- and site-specific influencing factors were analyzed using a generalized linear mixed model regression (GLMM). A total of 34 patients with 201 affected teeth were included, comprising78 cases of Cairo RT1, 86 of RT2, and 37 of RT3. Significant clinical improvements were observed over an average follow-up of 11.97 ± 4.11months, including reductions in GRD (from 2.13 ± 1.14mm to 0.48 ± 0.73mm, P < 0.001) and CAL (from 3.22 ± 1.16mm to 1.64 ± 0.90mm, P < 0.001) and increases in KGW (from 2.58 ± 1.28mm to 3.72 ± 1.66mm, P < 0.001), with a CRC of 59.2% and MRC of 77.26% ± 33.48%. The mean RES was 8.21 ± 1.74, with 83.3% of patients reporting an improved quality of life. GLMM identified recession type, age, and tooth arch as predictablefactorsfor clinical outcomes (P < 0.001). Within its limitations, this study suggests that the TUN + DCTG technique is a promising approach for managing GR, yielding favorable clinical and aesthetic outcomes. Clinical decision-making for GR treatment should consider a range of influencing factors to optimize individualized patient care.Clinical relevance: The TUN + DCTG technique is a reliable and effective option for GR treatment, providing consistent clinical and aesthetic benefits. Understanding predictive factors enables clinicians to tailor treatment strategies, improving success rates and patient satisfaction.

Endovascular recanalization of symptomatic chronic cerebral artery occlusion: predictors for successful recanalization and perioperative complications

Background and purposeEndovascular recanalization and stenting has been used to treat patients with symptomatic chronic cerebral artery occlusion, including intracranial vertebrobasilar artery occlusion and internal carotid artery occlusion. Our challenge is to improve success rates and reduce the incidence of postoperative complications. This study sought to identify potential predictors for successful recanalization.MethodsOur study included 103 consecutive patients between February 2021 and October 2024 with symptomatic chronic cerebral artery occlusion who were treated with endovascular recanalization. We recorded clinical information, laboratory and examination results, radiologic characteristics and procedural results of patients. Factors affecting surgical outcomes were analyzed by univariate and multivariate analyses.ResultsA total of 103 consecutive CCAO recanalization attempts were performed from February 2021 to October 2024 in 103 patients (78 men; age 61.1 ± 11.1 years; range: 32–81 years) with overall technical success rate 68.9%. Patients had chronic comorbidities such as hypertension (78, 75.7%), diabetes mellitus (32, 31.10%), and cardiac disease (12, 11.7%). 38 (36.9%) had a history of smoking, and 23 (22.3%) had a history of drinking. The rate of overall intraoperative complication was 10.7% (11/103). Multivariate analysis showed that stump morphology, smoking history, duration from last neurologic event (longer than 6 months or not), age, NLR were significantly associated with successful recanalization. According to the coefficients of the prediction model, the technical success rates were 100, 66.7 and 11.1% in patients with ≤6, 6–10, ≥10 points, respectively.ConclusionThe morphology of occluded stumps, duration from last neurologic event, age, smoking history and NLR can be used to predict the outcome of vascular recanalization.

Psychotropic Medication Use in 48,XXYY Syndrome

ABSTRACT48,XXYY syndrome is a rare sex chromosome aneuploidy (SCA) condition affecting 1 in 18,000–40,000 male births. Clinical features include tall stature, hypergonadotropic hypogonadism (testosterone deficiency), infertility, developmental delays, learning disabilities, and intellectual impairment. Co‐occurring behavior and mental health challenges are common in this population, with high rates of attention‐deficit hyperactivity disorder (ADHD), anxiety, depression, sleep disorders, irritability, and aggressive behaviors. We evaluated psychotropic medication use by parent report and retrospective chart review. Treatment success was defined as a positive response per parent report, positive clinician rating, or a treatment duration of at least 6 months. Nearly three‐quarters of participants (71/101) with a median age of 15.9 (range 4.5–38 years) had received psychotropic medications. The most common medication classes used were stimulant ADHD medications (78.9% of those with medication use), with a median age of first use of 9 years ([IQR] 7, 11 years), followed by anti‐anxiety/antidepressant medications (60.6%), with a median age of first use of 10 years ([IQR] 8, 14 years). Treatment success rates ranged from 43.9% to 84.2% for individual medication trials. Subsequent trials of medications within the same class improved success rates per person in all medication classes except for sleep and mood stabilizer medications. Treatment failure due to side effects was greatest among neuroleptics/atypical antipsychotics, whereas treatment failure due to inefficacy was greatest among anti‐anxiety/antidepressants and mood stabilizers. The findings of this study suggest that psychotropic medications targeting behavior and mental health are common and overall helpful for individuals with 48,XXYY.

USINE-PAMS Artificial Intelligence-Driven, Ultrasound-Guided Lumbar Puncture to Improve Procedural Accuracy: A Pilot Study Report.

Traditional lumbar punctures (LPs) often fail, leading to diagnostic delays and increased risks. Ultrasound guidance provides improved success rates but faces adoption barriers due to neuraxial-ultrasound training and implementation challenges. The Ultrasound-Guided Spinal Landmark Identification With Needle Navigation System and Position and Angular Marking System (uSINE-PAMS) were designed to address these issues: uSINE is a machine-learning software for neuraxial-ultrasound guidance; PAMS is a hardware that translates ultrasound data for accurate needle insertion. A pilot study with 10 patients showed that uSINE-PAMS-guided LP achieved an 80% first-pass success rate with no complication; the median patient age was 43 years, and the median body mass index was 24.5 kg/m2. The uSINE-PAMS system showed feasibility. This pilot study showed that uSINE-PAMS-guided LP is feasible with a promising first-pass success rate at 80%. An ongoing phase 2 study (NCT05824546) of uSINE-PAMS may alter future standard of practice for LPs. This pilot study is registered under ClinicalTrials.gov (ID: NCT05824546).

LLM based expert AI agent for mission operation management

Mission operation management is the coordination and control of various activities related to the operation of a satellite. This critical function involves planning, monitoring, controlling, and coordinating all aspects of the mission, ensuring the spacecraft achieves its objectives. Current mission operation management faces limitations in terms of data transmission relying solely on ground control, manual analysis procedures, and not capitalizing on technology for optimizing routine mission operations. This research proposal aims to develop a Large Language Model (LLM) based Expert AI agent for performing mission operation management. The proposed LLM based AI assistant will perform tasks such as data analysis for pattern recognition, operational planning, and document summarization. The system is designed to operate offline, providing flexibility in deployment. Integrating AI with mission operation management can benefit mission controllers and engineers, scientists and researchers, space agencies and organizations. AI offers opportunities to reduce mission costs, improve success rates, and enhance the efficiency of space exploration programs.

Progress of pediatric liver transplantation: In Japan and beyond.

Organ transplantation, particularly pediatric liver transplantation (LT), has transformed medical practice over the past six decades, providing life-saving interventions for children with end-stage liver disease. This review demonstrated the historical milestones of pediatric organ transplantation, emphasizing Japan's contributions, mainly through the National Center for Child Health and Development. While early transplantation efforts in the 1950s and 1960s faced significant challenges, breakthroughs in preservation methods, immunosuppressive therapies, surgical techniques, and innovations such as living donor LT in Asia have greatly improved success rates. Japan's pediatric LT landscape is distinct, primarily due to its reliance on living donor LT, shaped by cultural and religious influences that have traditionally restricted deceased donor organ donation. This review manuscript discusses Japan's pioneering role in expanding the indications for pediatric LT to include rare conditions such as inherited metabolic disorders and hepatoblastoma. It highlights recent innovations such as hyper-reduced lateral segment grafts, machine perfusion, and minimally invasive surgery that have further improved outcomes. International collaboration has facilitated the sharing of expertise, advancing pediatric live transplantation practice worldwide. Despite these achievements, challenges remain, particularly in light of Japan's declining birth rate, which threatens the sustainability of pediatric transplant services. This review emphasizes the need for centralized transplant facilities, greater awareness of brain-dead organ donation, and continued medical advances to ensure that pediatric LT remains a viable, life-saving option for future generations.

The genetics of preimplantation embryonic arrest: the role of aneuploidies.

Aneuploidy is a major cause of embryonic arrest. While meiotic aneuploidies, especially maternal, are a well-documented cause of embryo and fetal arrest, increasing evidence highlights the crucial role played by mitotic aneuploidies. This review explores the molecular and cellular pathways underlying these abnormalities, focusing on abnormal cleavage, chromatin cohesion, spindle stability, maternal effect genes, and mitochondria. Approximately half of human embryos cease development in vitro or shortly after transfer to the uterus. Genetic investigation of these embryos has highlighted that 90% of these exhibit aneuploidies. Surprisingly, most of these arise from errors during the early mitotic divisions of preimplantation embryos. These findings strongly correlate with disruptions of early cleavage possibly due to faulty spindle assembly or mitochondrial dysfunction during the in-vitro development. Moreover, maternal effects, such as faulty meiotic recombination and variants in maternal effect genes involved in the subcortical maternal complex, may further predispose the embryo to high rates of chromosomal imbalance. Meiotic and mitotic aneuploidies play a significant role in embryo arrest, yet their molecular and cellular origin are not well understood. Investigating these pathways may lead to interventions that could be developed to improve success rates with IVF or even fertility rates in general.

A Survey on Artificial Intelligence-Powered Drug Discovery and Development in Real-Life Environments Including Neonatal Therapeutics

The drug discovery and development process is a complex, time-consuming, and expensive endeavor, often taking over a decade and costing billions of dollars. Artificial Intelligence (AI) has emerged as a transformative tool in this domain, offering the potential to accelerate drug discovery, reduce costs, and improve success rates. This paper provides a comprehensive survey of AI-powered approaches in drug discovery and development, focusing on their real-life applications, challenges, and future directions. We explore how AI is being used in target identification, molecular design, clinical trials, and post-market surveillance, with special attention to neonatal drug development, where AI-driven models can aid in formulating safe, effective, and personalized treatments for newborns. The paper discusses the ethical, regulatory, and technical challenges that must be addressed for widespread adoption, particularly in the development of neonatal-specific therapeutics where precision and safety are critical. Additionally, we highlight case studies, emerging trends, and the integration of AI into real-world pharmaceutical workflows, emphasizing its role in improving drug repurposing, optimizing dosage formulations, and accelerating approval processes. By examining AI's impact on both general and neonatal drug discovery, this survey serves as a valuable resource for researchers, clinicians, and pharmaceutical experts aiming to leverage AI for next-generation therapeutics.

The Effect of Preoperative Tamsulosin on Pediatric Ureteroscopic Access: A Multi-institutional Experience.

The ability to perform flexible ureteroscopy in children may be limited because of a smaller pediatric ureterovesical junction and ureteral diameter. Tamsulosin has been shown to improve success rates of ureteral instrumentation in adults. To date, the efficacy of this medication to facilitate pediatric ureteral access remains unclear. We conducted a multi-institutional retrospective review of patients aged 0 to 17 years who underwent ureteroscopy for the treatment of nephrolithiasis from 2013 to 2022. Patients were excluded if they had undergone ureteroscopy or ureteral stent placement within the prior year, underwent semirigid ureteroscopy, or had a known genitourinary abnormality. Study groups included patients prescribed 0.4 mg tamsulosin daily for at least 1 week preoperatively and patients who did not receive tamsulosin. There were 382 patients included, with 126 in the tamsulosin group and 256 in the no-tamsulosin group. Although there were no differences in sex, race, and stone characteristics between the 2 groups, the tamsulosin group was significantly older and had a larger BMI. One week of preoperative tamsulosin was associated with a significantly increased success of flexible ureteroscopic access on first attempt ureteroscopy, with a 24% failure in the no-tamsulosin group vs a 13% failure in the tamsulosin group (P = .010). Our results expand on prior research and suggest that at least 1 week of preoperative tamsulosin facilitates flexible ureteroscopic access in the pediatric population. These results have significant clinical implications, with the potential to reduce multiple procedures and spare children from repeat anesthetic exposures.

Smart Drop: A Deep Learning Framework for Predicting College Dropouts

Educational Data Mining (EDM) plays a key role in improving modern learning by using advanced techniques. This study focuses on predicting college dropout rates with a deep learning framework. By analysing student data, our model helps identify at-risk students early, allowing timely support. The dataset contains student details such as academic performance, socioeconomic status, and engagement levels. Using these details, our framework applies deep learning methods to detect early signs of dropout. This helps universities take quick action to support students and improve their learning experience. Our study checked how well different models predicted dropout rates. The K-Nearest Neighbours (KNN) model had 93% accuracy, while the Recurrent Neural Network (RNN) had 95% accuracy. When KNN and RNN were combined, accuracy dropped to 87%. However, combining KNN and Convolutional Neural Network (CNN) improved accuracy to 97%. The RNN and CNN combination scored 96% accuracy, and the standalone CNN model also reached 97% accuracy. The best-performing method in this study is the KNN and CNN combination, which achieved the highest accuracy of 97%. Even though the CNN model alone also reached 97%, combining it with KNN helps capture more patterns in the data. This approach improves student retention by identifying and helping struggling students early. Our findings show that deep learning can effectively predict dropout rates, allowing educational institutions to provide better student support and improve success rates.

Development of drug discovery support system using chemoinformatics and generative AI technology

In recent years, the rapid development of generative AI has given rise to a variety of services such as machine translation, sentence summarization, and programming code generation. In drug discovery, generative AI and chemoinformatics have been used for seed/lead compound generation and optimization, and several successful cases were reported. The use of AI technology in drug discovery is expected to solve previously difficult problems and dramatically improve success rate in drug discovery. ITM, Inc. is a venture company established in 2004 to support drug discovery in silico using original chemoinformatics technology. Currently, ITM is developing a drug discovery support system that combines state-of-the-art AI technology and chemoinformatics technology. This paper introduces ITM's technology with a focus on the use of generative AI.

Improving success rates of remote conservation translocations by mitigating harsh in-situ environmental conditions: A case study on a Critically Endangered succulent

Improving success rates of remote conservation translocations by mitigating harsh in-situ environmental conditions: A case study on a Critically Endangered succulent

Rationally minimizing natural product libraries using mass spectrometry.

Natural product libraries are large collections of extracts derived from fungi, plants, bacteria, or any other natural sources. These libraries play an important role in the initial phases of drug discovery, providing the basis for bioassays against a target of interest. However, these collections often comprise thousands of extracts with sometimes overlapping chemical structures, which can result in a bottleneck in both time and costs for the initial phases of drug discovery. Here, we have developed a method that uses mass spectrometry to dramatically reduce the size of these libraries, with minimal tradeoffs and improved success rates in bioassays. Ultimately, this will speed up the process of bioactive candidate identification and isolation, and drug development overall.

Path Navigation for Multi-Agent Flight System Based on Distributed Model Predictive Control and Reinforcement Learning

The continuous change in formation configurations offers significant potential for formation flight. However, existing methods neglect the potential of autonomous navigation and learning ability based on environmental conditions, which would limit scalability for large formations to some extent. To address this, this paper presents an integrated framework that incorporates an innovative policy for configuration learning into multi-agent flight systems. Firstly, a decoupled trajectory optimization model is proposed, consisting of prediction and learning modules that ensure local adaptivity, obstacle avoidance and dynamic feasibility of formation configuration. Secondly, we design a stage cost function representing obstacle conditions, configuration variability and minimum distance constraints between agents. Then, to enhance adaptability in constrained scenarios, we introduce a reference value generation method that adjusts trajectory learning based on local observations, penalizing errors. Finally, we propose a configuration learning strategy called the local-level action planner and global cost, which coordinates local trajectory optimization with global maintenance. This method integrates distributed model predictive control (DMPC) with modified multi-agent proximal policy optimization (MAPPO), easing the challenge of tracking trajectories from varying rigid geometric shapes. Simulation results demonstrate that the proposed approach improves success rate compared to traditional methods while maintaining formation and avoiding obstacles.

Patients' experiences in ultrasound-guided intravenous catheter insertion: A qualitative study.

Patients' experiences in ultrasound-guided intravenous catheter insertion: A qualitative study.

An Overview of Bioavailability and Bioequivalence Study in Human Subject

Currently drug bioavailability has become a subject of interest not only in drug development, but also in the early stages of drug discovery. This is on sequence of the finding that most of the drugs candidate that failed in clinical trials did so because of problems with ADME (absorption, distribution, metabolism, excretion) and toxicology, rather than through lack of efficacy. Efforts are being made in the pharmaceutical industry to improve success rates by taking into account ADME and toxicology aspects in drug discovery from very early one. Therefore, it is not surprising to see that the number of publications on drug bioavailability has been increasing steadily for some time. In this review, attention is focused to briefly discuss some terms of bioavailability, relative bioavailability, bioequivalence, formulation and manufacturing variables that could influence the bioavailability of a drug product, physiologic and other factors affecting bioavailability, characteristics of drugs with the greater potential for a bioavailability problem, assessment of bioavailability from pharmacologic as well as therapeutic response, bioavailability of drugs versus dietary supplements, causes of low bioavailability and different approaches to improve it based on biopharmaceutical classification system. Thus approaches to improve drug solubility as well as drug permeability are the important strategies in order to enhance the bioavailability of drugs.

Guidelines for Performing Ultrasound-Guided Vascular Cannulation: Recommendations of the American Society of Echocardiography.

Guidelines for Performing Ultrasound-Guided Vascular Cannulation: Recommendations of the American Society of Echocardiography.

Measurement of mandibular anatomy associated with inferior alveolar nerve block anesthesia using 3D mandible models reconstructed by CBCT.

The objective was to evaluate the anatomical relationship between the mandibular foramen and surrounding structures using CBCT and to investigate the potential causes of inferior alveolar nerve block (IANB) failure. In this retrospective study, CBCT images of 222 mandibular sides from 111 patients (55 men, 56 women; aged 18-45 years) who underwent CBCT examination at the Shenzhen University General Hospital between January 2018 and December 2020 were analyzed. Three-dimensional models were reconstructed, and measurements of angles and distances related to the mandibular foramen were performed. The presence of a bony protuberance on the medial side of the mandibular ramus was evaluated. Differences between sexes were assessed using paired t tests or Wilcoxon rank-sum tests (α = .05). The angle between the anterior-posterior line of the mandibular ramus and the line connecting the innermost point to the mandibular foramen (angle AP-IF) was significantly greater than zero (P .05), indicating the presence of a bony protuberance. The angle between the midline and the line connecting the mandibular premolar contact point to the mandibular foramen (angle F45MnLP-ML) was 49.69 ± 2.17 degrees in men and 48.19 ± 2.20 degrees in women (P .001). The distance from the occlusal plane to the mandibular foramen was 9.45 ± 3.40 mm in men and 8.28 ± 3.41 mm in women (P = .011). The presence of a bony protuberance on the medial side of the mandibular ramus may contribute to IANB failure. Adjusting the needle insertion angle and height/vertical distance according to the reported measurements may improve IANB success rates. These findings may help clinicians optimize IANB techniques and improve success rates by adjusting needle insertion angles and heights/vertical distances based on individual patient anatomy.

Real-time lung extraction from synthesized x-rays improves pulmonary image-guided radiotherapy

Objective.Lung tumors can be obscured in x-rays, preventing accurate and robust localization. To improve lung conspicuity for image-guided procedures, we isolate the lungs in the anterior-posterior (AP) x-rays using a lung extraction network (LeX-net) that virtually removes overlapping thoracic structures, including ribs, diaphragm, liver, heart, and trachea.Approach.73 965 thoracic 3DCTs and 106 thoracic 4DCTs were included. The 3D lung volume was extracted using an open-source lung volume segmentation model. AP digitally reconstructed radiographs (DRRs) of the full anatomy CT and extracted lungs were computed as the input and reference to train a network (LeX-net) to generate lung-extracted DRRs (LeX-net DRRs) from full anatomy DRRs, which adopted a Swin-UNet model with conditional GAN. Subsequently, the trained LeX-net on 3DCT was applied to 4DCT-derived DRRs. Lung tumor tracking was then performed on DRRs using a template-matching method on a holdoff 4DCT test set of 79 patients whose gross tumor volumes were smaller than 20 cm3.Main results. LeX-net successfully isolated the lungs in DRRs, achieving an SSIM of 0.9581 ± 0.0151 and a PSNR of 30.78 ± 2.50 on the testing set of 3DCT-derived DRRs. Its performance declined slightly when applied to the 4DCT but maintained useable lung-only 2D views. On the challenging test set including cases of organ overlap, high tumor mobility, and small tumor size, the individual tumor tracking error for LeX-net DRRs was 0.97 ± 0.86 mm, significantly lower than that of 3.13 ± 5.82 mm using the full anatomy DRRs. LeX-net improved success rates of using 5 mm, 3 mm, and 1 mm tracking windows from 88.1%, 80.0%, and 58.7% to 98.1%, 94.2%, and 73.8%, respectively.Significance. LeX-net removes overlapping anatomies and enhances visualization of the lungs in x-rays. The model trained using 3DCTs is generalizable to 4DCT-derived DRRs, achieving significantly improved tumor tracking outcome.