Associated factors of low muscle mass in community-dwelling patients with type 2 diabetes.

This study aims to analyze the prevalence of low muscle mass (LMM) and its associated factors among community-dwelling patients with type 2 diabetes (T2D). A retrospective design was employed to analyze the body composition of 2404 adults with T2D aged 18 years and older. LMM was defined as a skeletal muscle index < 7.0 kg/m2 for males and skeletal muscle index < 5.7 kg/m2 for females by bioelectrical impedance analyzer (InBody 770, Korea). Multivariable logistic regression was used to identify the factors related to LMM. The overall prevalence of LMM was 28%. After adjusting for multivariate odds ratios, factors significantly associated with LMM in patients with diabetes include being older than 75 years, female, having a body mass index of <18 kg/m2, and increased percent body fat. We recommend regular LMM evaluations for T2D patients with the previously mentioned characteristics as part of diabetes care.

Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.

Background: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develop an interpretable deep learning (DL) model to predict adverse events in geriatric patients within 72 hours of hospitalization. Methods: The study used retrospective data (2017-2020) from a major medical center in Taiwan. It included non-trauma geriatric patients who visited the emergency department and were admitted to the general ward. Data preprocessing involved collecting prognostic factors like vital signs, lab results, medical history, and clinical management. A deep feedforward neural network was developed, and performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC). Model interpretation utilized the Shapley Additive Explanation (SHAP) technique. Results: The analysis included 127,268 patients, with 2.6% experiencing imminent intensive care unit transfer, respiratory failure, or death during hospitalization. The DL model achieved AUCs of 0.86 and 0.84 in the validation and test sets, respectively, outperforming the Sequential Organ Failure Assessment (SOFA) score. Sensitivity and specificity values ranged from 0.79 to 0.81. The SHAP technique provided insights into feature importance and interactions. Conclusion: The developed DL model demonstrated high accuracy in predicting serious adverse events in geriatric patients within 72 hours of hospitalization. It outperformed the SOFA score and provided valuable insights into the model's decision-making process.

Formulation and In Vitro Assessment of Polymeric pH-Responsive Nanogels of Chitosan for Sustained Delivery of Madecassoside.

Madecassoside, a triterpenoid saponin compound mainly isolated from the gotu kola herb (Centella asiatica), shows an extensive range of biological activities, including antiapoptotic, antioxidant, anti-inflammatory, moisturizing, neuroprotective, and wound healing effects. It has been highly used in the management of eczema, skin wounds, and other diseases. Due to poor oral bioavailability, membrane permeability, and intestinal absorption, the clinical application of the madecassoside is limited. Hence, a drug carrier system is needed that not only sustains the release of the madecassoside but also overcomes the drawbacks associated with its administration. Therefore, the authors prepared novel pH-responsive chitosan-based nanogels for the sustained release of madecassoside. Free radical polymerization technique was used for cross-linking of polymer chitosan and monomer methacrylic acid in the presence of cross-linker N',N'-methylene bis(acrylamide). The decrease in polymer crystallinity after polymerization and development of nanogels was demonstrated by XRD and FTIR analysis. The effects of nanogel contents on polymer volume, sol-gel analysis, swelling, drug loading, and release were investigated. Results indicated that high swelling and maximum release of the drug occurred at pH 7.4 compared to pH 1.2 and 4.6, indicating the excellent pH-sensitive nature of the engineered nanogels. High swelling and drug release were perceived with the integration of a high quantity of chitosan, while a decline was observed with the high integration of N',N'-methylene bis(acrylamide) and methacrylic acid contents. The same effects of nanogel contents were shown for drug loading too. Sol fraction was reduced, while gel fraction was enhanced by increasing the chitosan load, N',N'-methylene bis(acrylamide), and methacrylic acid. The Korsmeyer-Peppas model of kinetics was trailed by all nanogel formulations with non-Fickian diffusion. The results demonstrated that prepared nanogels can be employed for sustained release of the madecassoside.

A Novel Approach of Polyvinyl Alcohol/Acrylic Acid Based Hydrogels for Controlled Delivery of Diclofenac Sodium.

AIM AND OBJECTIVE: The aim of this study was to prepare polyvinyl alcohol/acrylic acid (PVA/AA) hydrogels for the controlled release of diclofenac sodium and to develop PVA/AA hydrogels as controlled release carriers to overcome not only the side effects of diclofenac sodium but also sustain its release for an extended period. BACKGROUND: Diclofenac sodium is employed for relieving pain and fever. The half-life of diclofenac sodium is very short (1-2 h). Hence, multiple intakes of diclofenac sodium are required to maintain a constant pharmacological action. Multiple GI adverse effects are produced as a result of diclofenac sodium intake. METHOD: A free radical polymerization technique was used for crosslinking PVA with AA in the presence of APS. EGDMA was used as a cross-linker. FTIR and XRD confirmed the preparation and loading of the drug by prepared hydrogels. An increase in the thermal stability of PVA was shown by TGA and DSC analysis. Surface morphology was investigated by SEM. Similarly, water penetration and drug loading were demonstrated by porosity and drug loading studies. The pH-sensitive nature of PVA/AA hydrogels was investigated at different pH values by swelling and drug release studies. RESULTS: The development and drug loading of PVA/AA hydrogels were confirmed by FTIR and XRD analysis. TGA and DSC indicated high thermal stability of prepared hydrogels as compared to unreacted PVA. SEM indicated a hard and compact network of developed hydrogels. The swelling and drug release studies indicated maximum swelling and drug release at high pH as compared to low pH values, indicating the pH-sensitive nature of prepared hydrogels. Moreover, we demonstrated that drug release was sustained for a prolonged time in a controlled pattern by prepared hydrogels by comparing the drug release of the developed hydrogels with the commercial product Cataflam. CONCLUSION: The results indicated that prepared PVA/AA hydrogels can be used as an alternative approach for the controlled delivery of diclofenac sodium.

A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis.

Human leukocyte antigen (HLA) genes play pivotal roles in numerous immunological applications. Given the immense number of polymorphisms, achieving accurate high-throughput HLA typing remains challenging. This study aimed to harness the human pan-genome reference consortium (HPRC) resources as a potential benchmark for HLA reference materials. We meticulously annotated specific four field-resolution alleles for 11 HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4 and -DRB5) from 44 high-quality HPRC personal genome assemblies. For sequencing, we crafted HLA-specific probes and conducted capture-based targeted sequencing of the genomic DNA of the HPRC cohort, ensuring focused and comprehensive coverage of the HLA region of interest. We used publicly available short-read whole-genome sequencing (WGS) data from identical samples to offer a comparative perspective. To decipher the vast amount of sequencing data, we employed seven distinct software tools: OptiType, HLA-VBseq, HISAT genotype, SpecHLA, T1K, QzType, and DRAGEN. Each tool offers unique capabilities and algorithms for HLA genotyping, allowing comprehensive analysis and validation of the results. We then compared these results with benchmarks derived from personal genome assemblies. Our findings present a comprehensive four-field-resolution HLA allele annotation for 44 HPRC samples. Significantly, our innovative targeted next-generation sequencing (NGS) approach for HLA genes showed superior accuracy compared with conventional short-read WGS. An integrated analysis involving QzType, T1K, and DRAGEN was developed, achieving 100% accuracy for all 11 HLA genes. In conclusion, our study highlighted the combination of targeted short-read sequencing and astute computational analysis as a robust approach for HLA genotyping. Furthermore, the HPRC cohort has emerged as a valuable assembly-based reference in this realm.

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

RAPID-ED: A predictive model for risk assessment of patient's early in-hospital deterioration from emergency department.

Introduction: The objective of this multi-center retrospective cohort study was to devise a predictive tool known as RAPID-ED. This model identifies non-traumatic adult patients at significant risk for cardiac arrest within 48 hours post-admission from the emergency department. Methods: Data from 224,413 patients admitted through the emergency department (2016-2020) was analyzed, incorporating vital signs, lab tests, and administered therapies. A multivariable regression model was devised to anticipate early cardiac arrest. The efficacy of the RAPID-ED model was evaluated against traditional scoring systems like National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) and its predictive ability was gauged via the area under the receiver operating characteristic curve (AUC) in both hold-out validation set and external validation set. Results: RAPID-ED outperformed traditional models in predicting cardiac arrest with an AUC of 0.819 in the hold-out validation set and 0.807 in the external validation set. In this critical care update, RAPID-ED offers an innovative approach to assessing patient risk, aiding emergency physicians in post-discharge care decisions from the emergency department. High-risk score patients (≥13) may benefit from early ICU admission for intensive monitoring. Conclusion: As we progress with advancements in critical care, tools like RAPID-ED will prove instrumental in refining care strategies for critically ill patients, fostering an improved prognosis and potentially mitigating mortality rates.

The initial experiences of junior nursing college students when communicating with children during pediatric clinical practicum: A phenomenological study.

PURPOSE: Nursing students feel stressed during pediatric clinical practicum due to limited communication encounters with hospitalized children. The purpose of this study was to describe junior nursing college students' experiences of communicating with children during pediatric clinical practicums. DESIGN AND METHODS: A qualitative phenomenological research design was used. Purposive sampling was used to recruit 18 junior nursing college students who completed their pediatric clinical practicum. Data were collected using semi-structured interviews and were analyzed using Colaizzi's seven-step method for data analysis. RESULTS: Three themes emerged from the data. (1) Difficulties in communicating during initial practicum: fear, rejection, self-doubt of communication abilities, and unfamiliarity with the application of communication techniques posed frustrations among nursing students. (2) Efforts to learn during practicum: self-empowerment, seeking a diverse support system, adjusting communication methods, and striving to establish good relationships allowed nursing students to adapt to the pediatric curriculum. (3) Effective communication at the later stages of practicum: mastering fundamental communication techniques and exercising pediatric therapeutic communication techniques allowed nursing students to feel accomplished. CONCLUSIONS: Junior nursing college students initially encountered difficulties and frustration when communicating with children during their pediatric clinical practicum. This study serves as a guide for educators of pediatric nursing to design courses on communication with hospitalized children. PRACTICE IMPLICATIONS: These findings could be used to develop foundation courses on communicating with children for first-time pediatric nursing practicum students; for example, formulating a course on therapeutic play for children that encompasses communication techniques, pediatric ward simulation, and introduction to therapeutic play.

Development of a non-destructive depth-selective quantification method for sub-percent carbon contents in steel using negative muon lifetime analysis.

The amount of C in steel, which is critical in determining its properties, is strongly influenced by steel production technology. We propose a novel method of quantifying the bulk C content in steel non-destructively using muons. This revolutionary method may be used not only in the quality control of steel in production, but also in analyzing precious steel archaeological artifacts. A negatively charged muon forms an atomic system owing to its negative charge, and is finally absorbed into the nucleus or decays to an electron. The lifetimes of muons differ significantly, depending on whether they are trapped by Fe or C atoms, and identifying the elemental content at the muon stoppage position is possible via muon lifetime measurements. The relationship between the muon capture probabilities of C/Fe and the elemental content of C exhibits a good linearity, and the C content in the steel may be quantitatively determined via muon lifetime measurements. Furthermore, by controlling the incident energies of the muons, they may be stopped in each layer of a stacked sample consisting of three types of steel plates with thicknesses of 0.5 mm, and we successfully determined the C contents in the range 0.20-1.03 wt% depth-selectively, without sample destruction.

Preparation, Swelling, and Drug Release Studies of Chitosan-based Hydrogels for Controlled Delivery of Buspirone Hydrochloride.

BACKGROUND: Buspirone is used for the management of depression and anxiety disorders. Due to its short half-life and low bioavailability, it requires multiple daily doses and is associated with some side effects. AIM: This study aimed to develop chitosan-based hydrogels as drug-controlled release carriers. OBJECTIVE: The objective of this study is to prepare chitosan-based hydrogels as controlled release carriers in order to overcome the side effects of buspirone HCl and improve patients' compliance and their life quality. METHODS: Polymer chitosan was polymerized with two monomers, acrylic acid and itaconic acid, to synthesize pH-sensitive hydrogel. The Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) analysis were performed to confirm the structure formation and thermal stability. Water penetration capability and loading of the drug were performed by porosity and drug loading studies. The swelling and dissolution tests were performed to analyze the pH-sensitive nature of the developed hydrogels. RESULTS: FTIR, TGA, and DSC demonstrated that the chitosan-based hydrogels were successfully prepared. An increase in water penetration and drug loading into the hydrogel network was seen with the high incorporation of chitosan, acrylic acid, and itaconic acid. The swelling and dissolution studies revealed that prepared hydrogel offered the greatest swelling and drug release at a high pH of 7.4. The swelling and drug release from the hydrogel were affected by the concentrations of the incorporated contents. A controlled release of the drug was achieved by using chitosan-based hydrogel as a delivery carrier compared to commercial tablets of buspirone. CONCLUSION: The results showed that the developed chitosan-based hydrogel can be considered one of the most suitable drug carrier systems for the controlled delivery of buspirone.

The Role of Autophagy in Type 2 Diabetic Kidney Disease Management.

Diabetic kidney disease (DKD), or diabetic nephropathy (DN), is one of the most prevalent complications of type 2 diabetes mellitus (T2DM) and causes severe burden on the general welfare of T2DM patients around the world. While several new agents have shown promise in treating this condition and potentially halting the progression of the disease, more work is needed to understand the complex regulatory network involved in the disorder. Recent studies have provided new insights into the connection between autophagy, a physiological metabolic process known to maintain cellular homeostasis, and the pathophysiological pathways of DKD. Typically, autophagic activity plays a role in DKD progression mainly by promoting an inflammatory response to tissue damage, while both overactivated and downregulated autophagy worsen disease outcomes in different stages of DKD. This correlation demonstrates the potential of autophagy as a novel therapeutic target for the disease, and also highlights new possibilities for utilizing already available DN-related medications. In this review, we summarize findings on the relationship between autophagy and DKD, and the impact of these results on clinical management strategies.

Tuning In-Plane Magnetic Anisotropy and Interfacial Exchange Coupling in Epitaxial La2/3Sr1/3CoO3/La2/3Sr1/3MnO3 Heterostructures.

Controlling the in-plane magnetocrystalline anisotropy and interfacial exchange coupling between ferromagnetic (FM) layers plays a key role in next-generation spintronic and magnetic memory devices. In this work, we explored the effect of tuning the magnetocrystalline anisotropy of La2/3Sr1/3CoO3 (LSCO) and La2/3Sr1/3MnO3 (LSMO) layers and the corresponding effect on interfacial exchange coupling by adjusting the thickness of the LSCO layer (tLSCO). The epitaxial LSCO/LSMO bilayers were grown on (110)o-oriented NdGaO3 (NGO) substrates with a fixed LSMO (top layer) thickness of 6 nm and LSCO (bottom layer) thicknesses varying from 1 to 10 nm. Despite the small difference (∼0.2%) in lattice mismatch between the two in-plane directions, [001]o and [11̅0]o, a pronounced in-plane magnetic anisotropy was observed. Soft X-ray magnetic circular dichroism hysteresis loops revealed that for tLSCO ≤ 4 nm, the easy axes for both LSCO and LSMO layers were along the [001]o direction, and the LSCO layer was characterized by magnetically active Co2+ ions that strongly coupled to the LSMO layer. No exchange bias effect was observed in the hysteresis loops. In contrast, along the [11̅0]o direction, the LSCO and LSMO layers displayed a small difference in their coercivity values, and a small exchange bias shift was observed. As tLSCO increased above 4 nm, the easy axis for the LSCO layer remained along the [100]o direction, but it gradually rotated to the [11̅0]o direction for the LSMO layer, resulting in a large negative exchange bias shift. Therefore, we provide a way to control the magnetocrystalline anisotropy and exchange bias by tuning the interfacial exchange coupling between the two FM layers.

Fabrication and In vitro Evaluation of Carbopol/Polyvinyl Alcohol-based pH-sensitive Hydrogels for Controlled Drug Delivery.

BACKGROUND: Diclofenac sodium has a short half-life (about 1.5 hours), requiring repeated administration, and as a result, serious complications, such as GI bleeding, peptic ulcer, and kidney and liver dysfunction, are generated. Hence, a sustained/controlled drug delivery system is needed to overcome the complications caused by the administration of diclofenac sodium. AIMS: This study aimed to fabricate and evaluate carbopol/polyvinyl alcohol-based pH-sensitive hydrogels for controlled drug delivery. OBJECTIVE: pH-sensitive carbopol/polyvinyl alcohol graft-poly(acrylic acid) hydrogels (Cp/PVA-g-PAa hydrogels) were developed for the controlled delivery of diclofenac sodium. METHODS: The combination of carbopol/polyvinyl alcohol, acrylic acid, and ethylene glycol dimethacrylate was used as polymer, monomer, and cross-linker, respectively. The effects of the formulation's composition on porosity, swelling index, and release pattern of diclofenac sodium from the developed hydrogels were investigated. RESULTS: An increase in porosity and swelling was observed with the increasing amounts of carbopol and acrylic acid, whereas polyvinyl alcohol showed the opposite effect. Due to the formation of a highly viscous system, the drug release decreased with the increasing concentrations of carbopol and polyvinyl alcohol while increased with increasing acrylic acid concentration. The pH-responsive properties of the fabricated hydrogels were demonstrated by dynamic swelling and drug release studies at three different pH values. Higher dynamic swelling and diclofenac sodium (model drug) release were found at high pH values compared to low pH values, i.e., pH 7.4 > 4.6 > 1.2, respectively. Cytotoxicity studies reported no toxic effect of the prepared hydrogels, thus indicating that the prepared hydrogels are safe to be used on clinical basis. CONCLUSION: The prepared carbopol/polyvinyl alcohol crosslinked hydrogel can be used as a promising carrier for the controlled release of drugs.

Xanthan-Gum/Pluronic-F-127-Based-Drug-Loaded Polymeric Hydrogels Synthesized by Free Radical Polymerization Technique for Management of Attention-Deficit/Hyperactivity Disorder.

Smart and intelligent xanthan gum/pluronic F-127 hydrogels were fabricated for the controlled delivery of atomoxetine HCl. Different parameters such as DSC, TGA, FTIR, XRD, SEM, drug loading, porosity, swelling index, drug release, and kinetics modeling were appraised for the prepared matrices of hydrogels. FTIR confirmed the successful synthesis of the hydrogel, while TGA and DSC analysis indicated that the thermal stability of the reagents was improved after the polymerization technique. SEM revealed the hard surface of the hydrogel, while XRD indicated a reduction in crystallinity of the reagents. High gel fraction was achieved with high incorporated contents of the polymers and the monomer. An increase in porosity, drug loading, swelling, and drug release was observed with the increase in the concentrations of xanthan gum and acrylic acid, whereas Pluronic F-127 showed the opposite effect. A negligible swelling index was shown at pH 1.2 and 4.6 while greater swelling was observed at pH 7.4, indicating a pH-responsive nature of the designed hydrogels. Furthermore, a higher drug release was found at pH 7.4 compared to pH 1.2 and 4.6, respectively. The first kinetics order was followed by the prepared hydrogel formulations. Thus, it is signified from the discussion that smart xanthan gum/pluronic F-127 hydrogels have the potential to control the release of the atomoxetine HCl in the colon for an extended period of time.

Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography.

Objectives: The aim of this study was to develop a deep-learning pipeline for the measurement of pericardial effusion (PE) based on raw echocardiography clips, as current methods for PE measurement can be operator-dependent and present challenges in certain situations. Methods: The proposed pipeline consisted of three distinct steps: moving window view selection (MWVS), automated segmentation, and width calculation from a segmented mask. The MWVS model utilized the ResNet architecture to classify each frame of the extracted raw echocardiography files into selected view types. The automated segmentation step then generated a mask for the PE area from the extracted echocardiography clip, and a computer vision technique was used to calculate the largest width of the PE from the segmented mask. The pipeline was applied to a total of 995 echocardiographic examinations. Results: The proposed deep-learning pipeline exhibited high performance, as evidenced by intraclass correlation coefficient (ICC) values of 0.867 for internal validation and 0.801 for external validation. The pipeline demonstrated a high level of accuracy in detecting PE, with an area under the receiving operating characteristic curve (AUC) of 0.926 (95% CI: 0.902-0.951) for internal validation and 0.842 (95% CI: 0.794-0.889) for external validation. Conclusion: The machine-learning pipeline developed in this study can automatically calculate the width of PE from raw ultrasound clips. The novel concepts of moving window view selection for image quality control and computer vision techniques for maximal PE width calculation seem useful in the field of ultrasound. This pipeline could potentially provide a standardized and objective approach to the measurement of PE, reducing operator-dependency and improving accuracy.

Synthesis and Evaluation of Alginate-Based Nanogels as Sustained Drug Carriers for Caffeine.

The objective of this study is to design a polymeric network of nanogels for sustained release of caffeine. Therefore, alginate-based nanogels were fabricated by a free-radical polymerization technique for the sustained delivery of caffeine. Polymer alginate was crosslinked with monomer 2-acrylamido-2-methylpropanesulfonic acid by crosslinker N',N'-methylene bisacrylamide. The prepared nanogels were subjected to sol-gel fraction, polymer volume fraction, swelling, drug loading, and drug release studies. A high gel fraction was seen with the increasing feed ratio of polymer, monomer, and crosslinker. Greater swelling and drug release were observed at pH 4.6 and 7.4 as compared to pH 1.2 due to the deprotonation and protonation of functional groups of alginate and 2-acrylamido-2-methylpropanesulfonic acid. An increase was observed in swelling, loading, and release of the drug with the incorporation of a high feed ratio of polymer and monomer, while a reduction was seen with the increase in crosslinker feed ratio. Similarly, an HET-CAM test was used to evaluate the safety of the prepared nanogels, which showed that the prepared nanogels have no toxic effect on the chorioallantoic membrane of fertilized chicken eggs. Similarly, different characterizations techniques such as FTIR, DSC, SEM, and particle size analysis were carried out to determine the development, thermal stability, surface morphology, and particle size of the synthesized nanogels, respectively. Thus, we can conclude that the prepared nanogels can be used as a suitable agent for the sustained release of caffeine.

Risk Factors of Ixekizumab-Induced Injection Site Reactions in Patients with Psoriatic Diseases: Report from a Single Medical Center.

Ixekizumab (Taltz®) is a humanized anti-IL-17A monoclonal antibody approved for the treatment of various inflammatory diseases including psoriasis and psoriatic arthritis. Despite the favorable efficacy and safety, ixekizumab is also known for its high incidence of injection site reactions (ISRs), ranging from 6% to 55% in different studies according to different definitions and studied population. However, specific risk factors for ixekizumab-induced injection site reactions in patients with psoriatic diseases had not been well studied. In this retrospective study, we found that overweight or obesity might be a protective predictor for the occurrence of ixekizumab-induced ISRs in patients with psoriatic disease. Meanwhile, having a positive family history of psoriasis might be a potential risk factor. Last but not least, patients with diarrhea following ixekizumab injection were associated with a higher risk of developing ISRs. Future high-quality studies with larger samples are warranted to verify the relationship.

Improvement in Skin Penetration Capacity of Linalool by Using Microemulsion as a Delivery Carrier: Formulation Optimization and In Vitro Evaluation.

Linalool is an aromatic oil with analgesic, anti-inflammatory and anti-UVB-induced skin damage effects. The aim of this study was to develop a linalool-loaded microemulsion formulation for topical application. In order to quickly obtain an optimal drug-loaded formulation, statistical tools of the response surface methodology and a mixed experimental design with four independent variables of oil (X1), mixed surfactant (X2), cosurfactant (X3) and water (X4) were used to design a series of model formulations in order to analyze the effect of the composition on the characteristics and permeation capacity of linalool-loaded microemulsion formulations and to obtain an appropriate drug-loaded formulation. The results showed that the droplet size, viscosity and penetration capacity of linalool-loaded formulations were significantly affected by formulation component proportions. The skin deposition amount of the drug and flux of such formulations expressively increased about 6.1-fold and 6.5-fold, respectively, when compared to the control group (5% linalool dissolved in ethanol). After 3 months of storage, the physicochemical characteristics and drug level did not show a significant change. The linalool formulation-treated rat skin showed non-significant irritation compared to skin treatments in the distilled-water-treated group. The results showed that specific microemulsion applications might be considered as potential drug delivery carriers for essential oil topical application.

Proof-of-Principle Experiment for Testing Strong-Field Quantum Electrodynamics with Exotic Atoms: High Precision X-Ray Spectroscopy of Muonic Neon.

To test bound-state quantum electrodynamics (BSQED) in the strong-field regime, we have performed high precision x-ray spectroscopy of the 5g-4f and 5f- 4d transitions (BSQED contribution of 2.4 and 5.2 eV, respectively) of muonic neon atoms in the low-pressure gas phase without bound electrons. Muonic atoms have been recently proposed as an alternative to few-electron high-Z ions for BSQED tests by focusing on circular Rydberg states where nuclear contributions are negligibly small. We determined the 5g_{9/2}- 4f_{7/2} transition energy to be 6297.08±0.04(stat)±0.13(syst) eV using superconducting transition-edge sensor microcalorimeters (5.2-5.5 eV FWHM resolution), which agrees well with the most advanced BSQED theoretical prediction of 6297.26 eV.

Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm.

OBJECTIVE: We aimed to develop a deep learning artificial intelligence (AI) algorithm to detect impacted animal bones on lateral neck radiographs and to assess its effectiveness for improving the interpretation of lateral neck radiographs. METHODS: Lateral neck radiographs were retrospectively collected for patients with animal bone impaction between January 2010 and March 2020. Radiographs were then separated into training, validation, and testing sets. A total of 1733 lateral neck radiographs were used to develop the deep learning algorithm. The testing set was assessed for the stand-alone deep learning AI algorithm and for human readers (radiologists, radiology residents, emergency physicians, ENT physicians) with and without the aid of the AI algorithm. Another radiograph cohort, collected from April 1, 2020, to June 30, 2020, was analyzed to simulate clinical application by comparing the deep learning AI algorithm with radiologists' reports. RESULTS: In the testing set, the sensitivity, specificity, and accuracy of the AI model were 96%, 90%, and 93% respectively. Among the human readers, all physicians of different subspecialties achieved a higher accuracy with AI-assisted reading than without. In the simulation set, among the 20 cases positive for animal bones, the AI model accurately identified 3 more cases than the radiologists' reports. CONCLUSION: Our deep learning AI model demonstrated a higher sensitivity for detection of animal bone impaction on lateral neck radiographs without an increased false positive rate. The application of this model in a clinical setting may effectively reduce time to diagnosis, accelerate workflow, and decrease the use of CT.