Bioengineering最新文献

Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models to enhance diagnostic accuracy. The YOLO 11n model was used to detect odontogenic sinusitis single tooth position in dental panoramic radiographs and achieved an accuracy of 98.2%. The YOLOv8n-cls model diagnosed odontogenic sinusitis with a final classification accuracy of 96.1%, achieving a 16.9% improvement over non-enhanced methods and outperforming recent studies by at least 4%. Additionally, in clinical applications, the classification accuracy for non-odontogenic sinusitis was 95.8%, while for odontogenic sinusitis it was 97.6%. The detection method developed in this study effectively reduces the radiation dose patients receive during CT imaging and serves as an auxiliary system, providing dentists with reliable support for the precise diagnosis of odontogenic sinusitis.

Cancer chemotherapeutics administered to cancer patients via traditional oral or parenteral routes often encounter poor bioavailability and severe systemic side effects. Skin delivery is a promising alternative route with reduced side effects and improved therapeutic efficacy and has gained significant attention in recent years. With conventional or deformable liposomal nanocarriers as a skin permeation strategy, cancer chemotherapeutics can be delivered via skin route, offering an option for more efficient therapy. This review summarizes the recent advances in liposome nanocarrier efficacy to enhance the skin delivery of chemotherapeutics with a wide range of physicochemical properties (log P_oct from -0.89 to 5.93, MW from 130 to 1415) in targeting local skin cancer, breast cancer, and tumor metastasis and delivering the drug to systemic circulation to treat distal cancers. The potential mechanisms of skin permeation enhancement by different type of liposomes are also discussed in this review.

Neurogenic lower urinary tract dysfunction (NLUTD) is a secondary complication of a wide range of neurological disorders, which affects patients' everyday life and self-efficacy. Some brain imaging studies have shown an overlap between motor activation of the pelvic floor and lower limbs. This systematic review sought to examine the possibility of improving overactive bladder outcomes through a conservative approach based on lower limb training. We conducted a systematic literature review, following the PRISMA guidelines. The following databases were searched: PEDro, PubMed, TRIP, Cochrane Library, EDS base index, Google Scholar, and CINAHL. The PEDro Scale and Cochrane Risk of Bias Assessment Tool were used to assess the overall study quality and sources of bias. A total of 5567 records were retrieved through the systematic search, of which 104 were sought for retrieval; two cohort studies and one randomized controlled trial were finally included. Urodynamics and specific bladder functionality questionnaires showed preliminary evidence of improvement following lower limb stimulation, implemented according to different treatment types (exoskeleton training and weight-suspension walking training). Lower limb-focused exercises showed promising results for improving bladder function, despite the small number of studies and small sample sizes. Future research should confirm this hypothesis using larger samples.

Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. A relevant application is the investigation of patient-reported outcome measures (PROMs) after spine surgery. Although a multitude of PROMs have been described and validated, there is currently no consensus regarding which questionnaires should be utilized. Additionally, studies have reported varying degrees of accuracy in predicting patient outcomes based on questionnaire responses. PROMs currently lack standardization, which renders them difficult to compare across studies. The purpose of this manuscript is to identify applications of machine learning to predict PROMs after spine surgery.

The alveolar ridge undergoes a loss in volume and atrophy after tooth extraction. Understanding the wound healing and bone regeneration process after tooth extraction is a key factor in the insertion of dental implants. Therefore, the aim of the present study was to analyze the socket healing process after the extraction of upper premolars based on cone beam computed tomography (CBCT) over six months. Special focus was placed on the morphological changes in the alveolar crest and within the socket. A retrospective analysis of patients in need of tooth extraction in the upper premolar region was performed in this study. All patients received flapless tooth extraction under local anesthesia and CBCT immediately after tooth extraction. Further CBCT analysis was performed after three months for the first group (n = 18) and after six months for the second group (n = 18). The results showed that all sockets underwent an inward movement of the defect walls towards the defect center, resulting in reduced total alveolar ridge volume and defect volume. This result was observed after three months and persisted after six months. The inward movement was quantified as a vertical socket collapse of up to 30.1 ± 9.0% after three months and 34.3 ± 6.7% after six months. The horizontal inward movement was quantified as a buccal socket collapse of 47.7 ± 12.3% after three months and 55.7 ± 29.1% after six months. New bone formation within the socket was evident, especially in the occlusal part of the socket. Additionally, bone formation was primarily observed as bone apposition along the socket walls and did not reach the defect center in most cases. The combination of socket collapse and bone apposition led to the formation of cavitations inside the socket that were mostly localized under the occlusal part. These novel findings with respect to socket collapse and formation of cavitation represent a paradigm shift and call for reconsidering the current understanding of socket healing. Based on the data, socket healing should be understood as a patient-specific process that requires 3D radiographic analysis for planning dental implants.

Background: Sensorineural hearing loss (SNHL) is a major contributor to hearing impairment, yet effective therapeutic options remain elusive. Mendelian randomization (MR) has proven valuable for drug repurposing and identifying new therapeutic targets. This study aims to pinpoint novel treatment targets for SNHL, exploring their pathophysiological roles and potential adverse effects. Methods: This research utilized the UKB-PPP database to access cis-protein quantitative trait locus (cis-pQTL) data, with SNHL data sourced from the FinnGen database as the endpoint for the MR causal analysis of drug targets. Colocalization analysis was employed to determine whether SNHL risk and protein expression share common SNPs. A phenotype-wide association analysis was conducted to assess the potential side effects of these targets. Drug prediction and molecular docking were subsequently used to evaluate the therapeutic potential of the identified targets. Results: Four drug target proteins significantly associated with sensorineural hearing loss (SNHL) were determined by Mendelian randomization (MR) analysis and co-localization analysis. These drug targets include LATS1, TEF, LMNB2, and OGFR and were shown to have fewer potential side effects when acting on these target proteins by phenotype-wide association analysis. Genes associated with sensorineural hearing loss are primarily implicated in the Hippo signaling pathway, cell-cell adhesion, and various binding regulatory activities and are involved in the regulation of cell proliferation and apoptosis. Next, drugs for the treatment of SNHL were screened by the DsigDB database and molecular docking, and the top 10 drugs were selected based on p-value. Among them, atrazine CTD 00005450 was identified as the most likely therapeutic target, followed by ampyrone HL60 DOWN and genistein CTD 00007324. In addition, LMNB2, LATS1, and OGFR could be intervened in by multiple drugs; however, fewer drugs intervened in TEF. Conclusion: This study has successfully identified four promising drug targets for SNHL, which are likely to be effective in clinical trials with minimal side effects. These findings could significantly streamline drug development for SNHL, potentially reducing the costs and time associated with pharmaceutical research and development.

Out-of-hospital cardiac arrest (OHCA) is a major public health burden due to its high mortality rate, sudden nature, and long-term impact on survivors. Consequently, there is a crucial need to create prediction models to better understand patient trajectories and assist clinicians and families in making informed decisions. We studied 107 adult OHCA patients admitted at an academic Emergency Department (ED) from 2018-2023. Blood samples and ocular ultrasounds were acquired at 1, 6, and 24 h after return of spontaneous circulation (ROSC). Six classes of clinical and novel variables were used: (1) Vital signs after ROSC, (2) pre-hospital and ED data, (3) hospital admission data, (4) ocular ultrasound parameters, (5) plasma protein biomarkers and (6) sex steroid hormones. A base model was built using 1 h variables in classes 1-3, reasoning these are available in most EDs. Extending from the base model, we evaluated 26 distinct neural network models for prediction of neurological outcome by the cerebral performance category (CPC) score. The top-performing model consisted of all variables at 1 h resulting in an AUROC score of 0.946. We determined a parsimonious set of variables that optimally predicts CPC score. Our research emphasizes the added value of incorporating ocular ultrasound, plasma biomarkers, sex hormones in the development of more robust predictive models for neurological outcome after OHCA.

The article collection entitled "Bioengineering in Remediation of Polluted Environments" was launched in September 2021 [...].

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The biomechanical mechanism of postoperative patellofemoral joint (PFJ) complications after open-wedge high tibial osteotomy (OWHTO) has not been investigated. This study was to determine the length changes in the patellar tendon (PT), medial patellotibial ligament (MPTL), medial patellofemoral ligament (MPFL), and quadriceps moment arm (QMA) during staircase motion before and after OWHTO. Computed tomography (CT) scans of 15 patients' lower extremities were used to reconstruct three-dimensional models, and magnetic resonance imaging (MRI) of the knee and hip joints was used to mark the soft tissue footprints. Then, such soft tissue lengths were quantified by a dual fluoroscopic imaging system (DFIS). Additionally, function scores were used to assess patient outcome changes. The results showed that there was a contraction of the PT after OWHTO due to its adhesion to the osteotomy site, causing PT length to be negatively correlated to the open-wedge angle. In addition, the shortening of the MPTL and QMA caused patellar instability and an imbalance in the strength of the lower extremities. Additionally, most knee function scores improved after OWHTO, except the Feller scores. Multiple methods should be considered to optimize surgical procedures, postoperative rehabilitation, and physical therapy.