bluuebunny/crossref_metadata_2025 huggingface dataset:
Risk Factors for Amputation in the Surgical Treatment of Hemophilic Osteoarthropathy: A 20-Year Single-Center Report
Authors: Xu Yiming , Feng Bin , Zhu Wei , Wang Yingjie , Weng Xisheng
Published: March 2022

Purpose. Hemophilic osteoarthropathy (HO) is a common cause of spontaneous bleeding in hemophiliacs. Surgical procedures are necessary for patients with severe HO. As a last resort, amputation is sometimes needed to treat complex HO cases. This study aimed to review the existing records of patients who underwent amputations in HO surgical treatment, summarize the risk factors, and provide relevant references for surgeons. Methods. We retrospectively reviewed the records of hemophilic patients received surgeries at Peking Union Medical College Hospital between 2000 and 2020. The amputation patients without neoplasm or acute trauma were screened out. Patient information was extracted from medical records. A literature retrieval of hemophilic amputation cases was conducted via PubMed. The risk factors of amputation were summarized and analyzed via descriptive statistics and Fisher’s precision probability test. Results. Four male hemophilia A patients out of 201 patients underwent lower limb amputation. The reasons of amputation contained severe pseudotumor with factor inhibitor and large bone defects, recurrent pathological fracture with pseudotumor, skin ulcer with chronic osteomyelitis, and pseudotumor with systematic infection. In cases reported in retrieved literature, severe pseudotumor with complications, bacterial infection and factor inhibitor were common factors. Discussion. As the first study on amputations in HO patients, we found that severe hemophilic pseudotumor, chronic bacterial infection, and coagulation factor inhibitor was potential risk factors for amputation. Sufficient factor replacement therapy is fundamental in the prevention of amputation. The early diagnosis and specially designed surgical techniques could improve the rate of limb salvage.
CT Image Features Based on the Reconstruction Algorithm for Continuous Blood Purification Combined with Nursing Intervention in the Treatment of Severe Acute Pancreatitis
Authors: Liu Yanyan , Gu Mingli , Liu Liping , Cui Lunmeng , Xing Aimin
Published: March 2022

The aim of the study was to explore the CT images of the iterative reconstruction algorithm to evaluate the curative effect of continuous blood purification combined with nursing intervention in the treatment of severe acute pancreatitis (SAP). A total of 100 patients with SAP treated by the bedside continuous venous hemofiltration purification method in a hospital were selected. The control group (n = 50) was given a routine treatment, and the observation group (n = 50) was treated with the continuous blood filtration mode for blood purification based on the routine treatment. In the CT image scanning of periodontitis patients, the iterative reconstruction algorithm was introduced to reduce image noise, and the CT values under the algorithm were statistically analyzed. The results showed that IL‐1, IL‐6, and IL‐8 after treatment were significantly lower than those before treatment (P < 0.05). The symptoms effectively improved with continuous blood purification combined with nursing intervention in patients with SAP. After the use of the iterative reconstruction algorithm, the image quality, image information, and image MSE significantly improved. The image noise with 50% dose reduction was the lowest, but the reconstruction algorithm improved the low contrast resolution (P < 0.05). CT images based on the reconstruction algorithm can clearly display the lesion characteristics of the patients, and the reconstruction algorithm is feasible to improve the spatial resolution of CT images.
Automatic Traffic State Recognition Based on Video Features Extracted by an Autoencoder
Authors: Cai Xiaoyu , Jing Qiongli , Peng Bo , Zhang Yuanyuan , Wang Yuting , Tang Ju
Published: March 2022

Video surveillance has become an important measure of urban traffic monitoring and control. However, due to the complex and diverse video scenes, traffic data extraction from original videos is a sophisticated and difficult task, and corresponding algorithms are of high complexity and calculation cost. To reduce the algorithm complexity and subsequent computation cost, this study proposed an autoencoder model which effectively reduces the video dimension by optimizing structural parameters; thus several traffic recognition models can conduct image processing work based on dimension-reduced videos. Firstly, an optimal autoencoder model

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with five hidden layers was constructed. Then, it was combined with a linear classifier, support vector machine, deep neural network, DNN linear classification method, and the k-means clustering method; thus, five traffic state recognition models were constructed:

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-Linear,

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-SVM,

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-DNN,

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-DNN_Linear, and

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-k-means. Train and test results show that the accuracy rate and recall rate of

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-linear,

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-SVM,

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-DNN, and

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-DNN_Linear were 94.5%–97.1%, and the F1 score was 94.4%–97.1%; besides, the accuracy rate, recall rate, and F1 score of

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-k-means were all approximately 95%, which suggests that the combination of the autoencoder

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and common classification or clustering methods achieve good recognition performance. Comparison was also implemented among the five models proposed above and four CNN-based models such as AlexNet, LeNet, GoogLeNet, and VGG16, which shows that the five proposed modes achieve F1 scores of 94.4%–97.1%, while the four CNN-based models achieve F1 scores of 16.7%–94%, indicating that the proposed light weight design methods outperform more complex CNN-based models in traffic state recognition.
Load Transfer Efficiency Analysis and Void Evaluation of Composite Pavement Cement Concrete Slab
Authors: Li Hao , Fang Naren , Wang Xuancang , Fang Yang , Li Xianghang , Li Chao , Ding Siyin
Published: March 2022

The void at the bottom of a cement concrete slab is a common problem of composite pavements. It is of great significance to evaluate the spring stiffness, void size, and soil base elastic modulus at the bottom of cement concrete slab timely and accurately for the maintenance and guarantee of the pavement performance. In this paper, the composite pavement at a joint is modeled and the load transfer mechanism is realized by using spring groups for simulating the dowels that connect the two slabs of the joint. A database is established based on a large number of simulation results of joint load transfer efficiency and voids and the changing law of load transfer efficiency of cement concrete slab reveals their relationship. Taking into consideration the spring stiffness, void size, and soil base elastic modulus, the void evalua\tion method of composite pavement cement concrete slab is established by using back propagation neural network algorithm. The results show that the void evaluation method can provide the void determination map with two parameters. The values of the two parameters can be determined by the deflection basin. After querying the Atlas, the void determination can be more accurate. The research results provide scientific guidance for the void identification of the composite pavement and can effectively extend the service life of the pavement.
Intelligent Somatosensory Interactive Activities Restore Motor Function to Children with Autism
Authors: Wang Qiang , Wang Xing , Xu Lei
Published: March 2022

So far, the biomedical community has not provided clear etiological conclusions and targeted drug treatments. Educational intervention and rehabilitation are the main ways to promote the development of autistic children’s ability and change the quality of life. This research mainly explores how intelligent somatosensory interactive activities can restore motor function to children with autism. Case studies are used to investigate the current problems of hand movement training for children with autism and the effect of somatosensory games on rehabilitation training for autism. The experience of autistic children using somatosensory games for hand movement training was analyzed. Through the collection, sorting, and analysis of data, the influence of different factors on users’ immersive experience is explored. It designs and implements the system’s somatosensory game module, including a detailed introduction to the development platform and key technologies used in the development of the somatosensory game module, and shows the functions, program flow, main features, and implementation effects of the somatosensory game. The development process of the somatosensory interaction system is introduced in detail, including model making, character control, task flow control, collision detection, interactive interface, and natural interaction methods of gesture interaction and voice interaction. This study outlines the concepts related to autism and the characteristics of children with autism. It discusses the feasibility of applying somatosensory games to the hand movement training of children with autism and analyzes the development status and application of somatosensory games in detail to lay the foundation for follow-up research. Moreover, it defines the research content of somatosensory interactive training products and clarifies the design content and direction of the product. The comfort evaluation of the somatosensory game products designed in the study reached 92.9%. This research further proves that somatosensory games have a positive effect.
Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier
Authors: Alnuaim Abeer Ali , Zakariah Mohammed , Shukla Prashant Kumar , Alhadlaq Aseel , Hatamleh Wesam Atef , Tarazi Hussam , Sureshbabu R. , Ratna Rajnish
Published: March 2022

Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker’s inner condition and his/her aim and desire. While word analysis enables the speaker’s request to be understood, other speech features disclose the speaker’s mood, purpose, and motive. As a result, emotion recognition from speech has become critical in current human-computer interaction systems. Moreover, the findings of the several professions involved in emotion recognition are difficult to combine. Many sound analysis methods have been developed in the past. However, it was not possible to provide an emotional analysis of people in a live speech. Today, the development of artificial intelligence and the high performance of deep learning methods bring studies on live data to the fore. This study aims to detect emotions in the human voice using artificial intelligence methods. One of the most important requirements of artificial intelligence works is data. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) open-source dataset was used in the study. The RAVDESS dataset contains more than 2000 data recorded as speeches and songs by 24 actors. Data were collected for eight different moods from the actors. It was aimed at detecting eight different emotion classes, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised moods. The multilayer perceptron (MLP) classifier, a widely used supervised learning algorithm, was preferred for classification. The proposed model’s performance was compared with that of similar studies, and the results were evaluated. An overall accuracy of 81% was obtained for classifying eight different emotions by using the proposed model on the RAVDESS dataset.
Novel Key Indicators Selection Method of Financial Fraud Prediction Model Based on Machine Learning Hybrid Mode
Authors: Xu Hongsheng , Fan Ganglong , Song Yanping
Published: March 2022

In recent years, the financial fraud event of listed companies has occurred continuously. The financial fraud has brought huge losses to the capital market and investors, hindering the investment allocation mechanism of the capital market. The current financial fraud prediction model can judge the company that may conduct financial fraud in advance. So, this can reduce economic losses. The key factor to construct the financial fraud prediction model is how to select the evaluation indicators. This paper analyzes the existing indicators selection method and finds the problem of low prediction accuracy. A key indicators selection method of prediction model based on machine learning hybrid mode is proposed. First, the contribution degree of the selected algorithm and model is ranked according to the features. The support vector machine with good classification effect and heterogeneity with other models is used as the intermediate evaluation model. A variety of selected indicators from machine learning are tested for AUC on the intermediate model. Well-performing machine learning models are selected and combined into multiple hybrid modes. These hybrid models are tested for AUC again. Experiments demonstrate that the hybrid mode of Lasso method and random forest performed best in the AUC test. The repetition indicators of the hybrid model are then selected as important indicators of the prediction model. Finally, the correlation of the indicators is tested, and the indicators beyond the threshold are removed. The selected key indicators effectively improve the accuracy of financial fraud prediction.
Recognition and Prediction of Badminton Attitude Based on Video Image Analysis
Authors: He Songsong , Gong Ruyao
Published: March 2022

Among many sports, badminton is one of the most popular events and it is deeply loved by people. However, there is relatively little research on pose recognition and prediction of badminton, so this paper uses video image analysis to perform badminton action recognition prediction and action classification. In order to better realize the lower limb movement pattern recognition and motion posture prediction of badminton players, this paper chooses BP neural network algorithm to establish a badminton motion posture recognition and prediction model based on video image analysis. The simulation results of the models are compared, and it is found that the recognition of the motion pose and the prediction model established by the neural network are accurate, and they almost coincide with the actual motion trend. In this paper, a face detection and recognition framework is established. The algorithm is used to filter the pictures of the input recognition network. Through calculation, the average accuracy rate of the face detection algorithm realized in this paper can reach 92.6%. This shows that the face detection algorithm implemented in this paper basically meets the standards. In this paper, a single inertial sensor is used to classify and recognize badminton movements using sensors located on the right wrist, left wrist, waist, and right ankle. This shows that the right wrist is the best position and achieves different strokes.
Observation on Application Effect of Arterial Puncture and Catheterization under Guidance of Intelligent Medical Care Ultrasound in Clinical Anesthesia
Authors: Ren Guozhang , Qiang Xiancheng , Yu Hui
Published: March 2022

In clinical anesthesia and the rescue of critically ill patients, arterial puncture and catheterization are the most commonly chosen ways to establish central arterial access for patients. Invasive arterial puncture and catheterization facilitate the grasp of real-time vital sign information of patients during surgery, which strengthens patient monitoring during surgery and improves safety. However, the traditional method of arterial puncture and cannulation through palpation of the radial artery is often prone to complications related to mechanical injury, such as hemorrhage, hematoma, and accidental perforation of the artery. Studies have shown that ultrasound-guided radial artery puncture and cannulation can shorten the puncture cannulation time, reduce the incidence of complications related to puncture cannulation, and improve the success rate of puncture cannulation. In order to verify it, this paper uses the experimental group and the control group to conduct comparative experiments and uses the neural network method to evaluate the effects of the two methods. As a more mature method of artificial intelligence, BP neural network is widely used in a wide range of applications and has the characteristics of strong generalization ability and fast convergence, so we choose it as the base model. The specific work of this paper is as follows: (1) in-depth study of the relevant theory of BP neural network (BPNN), focusing on the structure of BPNN and the working principle of algorithm; the problems to be solved in the clinical anesthesia effect evaluation have laid a theoretical foundation for the establishment of an improved BPNN evaluation model in the following chapters. (2) introduce the basic principle of genetic neural network, analyze the benefits of combining genetic neural network and BPNN; introduce in detail the process of genetic algorithm to optimize the weights and thresholds of BPNN, and establish a GA-BP evaluation model. The test proves the feasibility and superiority of the model.
Structural Damage Identification Method Based on Parametric Boundary Condition Optimization
Authors: Guo Wei , Jiang Wei
Published: March 2022

According to the heterogeneous background interference, the subjective consciousness, inaccurate quantification, and high cost in practical engineering, a method of applying the Bouc–Wen hysteresis nonlinear model to hysteresis characteristics of rubber isolation bearings is proposed, and this method analyzes the sensitivity of nonlinear parameters of the Bouc–Wen hysteresis model and obtains the order of nonlinear parameters of hysteresis model under different sizes of excitation. The results show that the prediction error of fatigue life is 16% less under various stress ranges, a rectangular identification frame with a certain probability guarantee rate is used to realize multitype seismic damage classification and location, the average accuracy of the identification of concrete cracking and spalling, steel bar exposure, and buckling is more than 80%, and the average coverage rate is more than 88%. During the use of civil engineering structures, the working environment is constantly changing; combined with the current damage identification and detection methods, the structure health monitoring system is established, and it is of great significance to promote the practical development of structural damage identification methods to accurately monitor the structure state in real time and to make a rapid and accurate diagnosis when the structure damage occurs.
Three-Rooted Permanent Mandibular First Molars: A Meta-Analysis of Prevalence
Authors: Aung Nyan M. , Myint Kyaw K.
Published: March 2022

Introduction. Although numerous amounts of high-level evidence were present, they solely emphasized the tooth-level prevalence of three-rooted permanent mandibular first molar. Global patient-level prevalence and bilateral symmetrical distribution of this type of teeth were needed to be tackled across the world. The research question was “What is the global prevalence of three-rooted permanent mandibular first molars?” Materials and Methods. In vivo epidemiological studies undergone with Cone Beam Computed Tomography (CBCT) were eligible. The proportions of the prevalence of three-rooted permanent mandibular first molars were presented in the forest plots by random effect model. The calculation was performed with MetaXL version 5.3. Subgroup analysis, sensitivity analysis, and publication bias method were also calculated. Results. Seventy-two studies from 31 countries were selected for both qualitative and quantitative analyses. 26302 patients and 37994 permanent mandibular first molars were included in the analysis. 9% of permanent mandibular first molars all over the world demonstrated 3 roots. These three-rooted teeth were found in 10% of the world population, more than 45% of which revealed bilateral symmetry of that anatomy. Right-side dominance and no sexual dimorphism were seen in the distribution of three-rooted permanent mandibular first molars. Global tooth-level prevalence of Radix Entomolaris and Radix Paramolaris was 12% and 0.1%, respectively. Conclusion. The prevalence of three-rooted permanent mandibular first molars (PMFMs) was influenced by different geographical locations across the world and also by widespread habitation of the Mongoloid descent. The authors postulate that globalization, together with blending among ethnicities, may have a great impact on the reduction or accentuation of the anatomical significance in some populations.
Cerebral Venous Thrombosis as an Initial Presentation of Ulcerative Colitis
Authors: Yamazaki Hiroshi , Sasaki Akinori , Yamaguchi Eriko , Sawada Kana , Okamoto Risa , Saigusa Kuniyasu , Motomura Yasuaki
Published: March 2022

Cerebral venous thrombosis (CVT) is a rare complication of ulcerative colitis (UC) that is potentially fatal once it occurs. This report describes a case of CVT that led to a diagnosis of UC. A 48-year-old woman was diagnosed with CVT due to paresthesia and weakness and was hospitalized for treatment. She developed bloody diarrhea on admission and was further diagnosed with UC based on endoscopic and pathologic findings. Treatment of UC with steroids and sulfasalazine was administered immediately. Her condition improved significantly within several days following treatment. After discharge, the patient experienced no recurrence of either CVT or UC flare-up over the last five years. This report describes CVT as an initial presentation of UC. This is also the first report of a long-term follow-up following successful treatment of CVT with concomitant UC.
SDBMND: Secure Density-Based Unsupervised Learning Method with Malicious Node Detection to Improve the Network Lifespan in Densely Deployed WSN
Authors: Sharma Tripti , Mohapatra Amar Kumar , Tomar Geetam
Published: March 2022

Random deployment, the absence of central authority, and the autonomous nature of the network make wireless sensor networks (WSNs) prone to security threats. Security, bandwidth, poor connectivity, intrusion, energy constraints, and other challenges are critical and could affect the performance of the WSN while considering the energy-efficient and secure routing protocols in WSNs. Security threats to WSNs are gradually being expanded. Thus, to improve the network’s performance, detection of anomalies (malicious and suspicious nodes, redundant data, bad connections, etc.) is important. This paper is aimed at introducing the malicious node detection algorithm based on the DBSCAN algorithm, which is a density-based unsupervised learning method for enabling wireless sensor networks to be much more secure and reliable. The prime objective of this algorithm is to develop a routing algorithm capable of detecting malicious nodes and having a prolonged network lifespan and higher stability period. Clustering and classification are two well-known methods in the field of machine learning that can be successfully used in various domains. Density-based clustering is a popular and extensively used approach in various domains. The DBSCAN is the utmost popular and best-known density-based clustering algorithm and is capable of determining arbitrary-shaped clusters. This paper addresses the two anomalies in the WSN, namely, spatial redundancy and malicious node identification. In this article, an algorithm has been suggested to reduce redundant data transmission along with the identification of suspicious nodes to conserve energy and to avoid falsification of data through malicious nodes. The analysis of simulation results and comparison of other algorithms that are in the same class shows that the SDBMND performs significantly better than EAMMH, TEEN, IC-ACO, and LEACH in dense networks.
Study on the Influence of Train Dynamic Load on underneath Tunnel Cast-in-Situ Concrete in Early Age
Authors: Zheng Yuchao , Liao Baolin , Shen Fei , Zhong Heng , Yang Jianfeng , Liu Kai , Gong Lun
Published: March 2022

The cast-in-situ concrete lining is prone to structural damage, cracks, and other defects, due to the influence of dynamic load at an early age, which is necessary to study the dynamic response. In this study, the finite difference software FLAC3D is used to carry out the dynamic analysis. Firstly, the reliability of the numerical model is verified by the train vibration test on-site. On this basis, the control variable method is used to analyze the dynamic response of the dynamic train load in early-age concrete structures under the factors of tunnel buried depth, train speed, cross angle, and surrounding rock grade. Then, the safety of the structure is evaluated. The numerical simulation results show that the influence of buried depth and surrounding rock on the early age structure is more obvious. Peak acceleration decreases with the increase in tunnel buried depth and increases with the increase in surrounding rock grade and train speed. Peak acceleration changes a little in the range of 0°–67.5°, but when the cross angle is 90°, the dynamic response reaches the minimum value. Multiple factors fitted the dynamic tensile stress, and the dynamic tensile stress under adverse conditions was calculated, compared with the splitting tensile strength of early-age concrete. The results show that the splitting tensile failure does not occur when the concrete age is more than 0.5 days.
Infigratinib in Patients with Recurrent Gliomas and FGFR Alterations: A Multicenter Phase II Study
Authors: Lassman Andrew B. , Sepúlveda-Sánchez Juan Manuel , Cloughesy Timothy F. , Gil-Gil Miguel J. , Puduvalli Vinay K. , Raizer Jeffrey J. , De Vos Filip Y.F. , Wen Patrick Y. , Butowski Nicholas A. , Clement Paul M.J. , Groves Morris D. , Belda-Iniesta Cristóbal , Giglio Pierre , Soifer Harris S. , Rowsey Steven , Xu Cindy , Avogadri Francesca , Wei Ge , Moran Susan , Roth Patrick
Published: March 2022

Purpose:

FGFR genomic alterations (amplification, mutations, and/or fusions) occur in ∼8% of gliomas, particularly FGFR1 and FGFR3. We conducted a multicenter open-label, single-arm, phase II study of a selective FGFR1–3 inhibitor, infigratinib (BGJ398), in patients with FGFR-altered recurrent gliomas.

Patients and Methods:

Adults with recurrent/progressive gliomas harboring FGFR alterations received oral infigratinib 125 mg on days 1 to 21 of 28-day cycles. The primary endpoint was investigator-assessed 6-month progression-free survival (PFS) rate by Response Assessment in Neuro-Oncology criteria. Comprehensive genomic profiling was performed on available pretreatment archival tissue to explore additional molecular correlations with efficacy.

Results:

Among 26 patients, the 6-month PFS rate was 16.0% [95% confidence interval (CI), 5.0–32.5], median PFS was 1.7 months (95% CI, 1.1–2.8), and objective response rate was 3.8%. However, 4 patients had durable disease control lasting longer than 1 year. Among these, 3 had tumors harboring activating point mutations at analogous positions of FGFR1 (K656E; n = 2) or FGFR3 (K650E; n = 1) in pretreatment tissue; an FGFR3-TACC3 fusion was detected in the other. Hyperphosphatemia was the most frequently reported treatment-related adverse event (all-grade, 76.9%; grade 3, 3.8%) and is a known on-target toxicity of FGFR inhibitors.

Conclusions:

FGFR inhibitor monotherapy with infigratinib had limited efficacy in a population of patients with recurrent gliomas and different FGFR genetic alterations, but durable disease control lasting more than 1 year was observed in patients with tumors harboring FGFR1 or FGFR3 point mutations or FGFR3-TACC3 fusions. A follow-up study with refined biomarker inclusion criteria and centralized FGFR testing is warranted.
Phase II Multi-institutional Clinical Trial Result of Concurrent Cetuximab and Nivolumab in Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma
Authors: Chung Christine H. , Li Jiannong , Steuer Conor E. , Bhateja Priyanka , Johnson Matthew , Masannat Jude , Poole Maria I. , Song Feifei , Hernandez-Prera Juan C. , Molina Helen , Wenig Bruce M. , Kumar Sunil , Kuperwasser Charlotte , Stephens Philip J. , Farinhas Joaquim M. , Shin Dong M. , Kish Julie A. , Muzaffar Jameel , Kirtane Kedar , Rocco James W. , Schell Michael J. , Saba Nabil F. , Bonomi Marcelo
Published: March 2022

Purpose:

A phase II multi-institutional clinical trial was conducted to determine overall survival (OS) in patients with recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) treated with a combination of cetuximab and nivolumab.

Patients and Methods:

Patients with R/M HNSCC were treated with cetuximab 500 mg/m2 i.v. on day 14 as a lead-in followed by cetuximab 500 mg/m2 i.v. and nivolumab 240 mg i.v. on day 1 and day 15 of each 28-day cycle. Expression of p16 and programmed cell death-ligand 1 (PD-L1) in archived tumors were determined. Tumor-tissue–modified human papillomavirus (TTMV) DNA was quantified in plasma.

Results:

Ninety-five patients were enrolled, and 88 patients were evaluable for OS with a median follow-up of 15.9 months. Median OS in the 45 patients who had prior therapy for R/M HNSCC (cohort A) was 11.4 months, with a 1 year OS 50% [90% confidence interval (CI), 0.43–0.57]. Median OS in the 43 patients who had no prior therapy (cohort B) was 20.2 months, with a 1-year OS 66% (90% CI, 0.59–0.71). In the combined cohorts, the p16-negative immunostaining was associated with higher response rate (RR; P = 0.02) but did not impact survival while higher PD-L1 combined positive score was associated with higher RR (P = 0.03) and longer OS (log-rank P = 0.04). In the p16-positive patients, lower median (1,230 copies/mL) TTMV DNA counts were associated with higher RR (P = 0.01) and longer OS compared with higher median (log-rank P = 0.05).

Conclusions:

The combination of cetuximab and nivolumab is effective in patients with both previously treated and untreated R/M HNSCC and warrants further evaluation.
Preclinical and Pilot Study of Type I FLT3 Tyrosine Kinase Inhibitor, Crenolanib, with Sorafenib in Acute Myeloid Leukemia and FLT3-Internal Tandem Duplication
Authors: Inaba Hiroto , van Oosterwijk Jolieke G. , Panetta John C. , Li Lie , Buelow Daelynn R. , Blachly James S. , Shurtleff Sheila , Pui Ching-Hon , Ribeiro Raul C. , Rubnitz Jeffrey E. , Pounds Stanley , Baker Sharyn D.
Published: March 2022

Purpose:

To evaluate the safety, activity, and emergence of FLT3-kinase domain (KD) mutations with combination therapy of crenolanib and sorafenib in acute myeloid leukemia (AML) with FLT3-internal tandem duplication (ITD).

Patients and Methods:

After in vitro and xenograft efficacy studies using AML cell lines that have FLT3-ITD with or without FLT3-KD mutation, a pilot study was performed with crenolanib (67 mg/m2/dose, three times per day on days 1–28) and two dose levels of sorafenib (150 and 200 mg/m2/day on days 8–28) in 9 pediatric patients with refractory/relapsed FLT3-ITD–positive AML. Pharmacokinetic, pharmacodynamic, and FLT3-KD mutation analysis were done in both preclinical and clinical studies.

Results:

The combination of crenolanib and sorafenib in preclinical models showed synergy without affecting pharmacokinetics of each agent, inhibited p-STAT5 and p-ERK for up to 8 hours, and led to significantly better leukemia response (P < 0.005) and survival (P < 0.05) compared with single agents. Fewer FLT3-KD mutations emerged with dose-intensive crenolanib (twice daily) and low-intensity sorafenib (three times/week) compared with daily crenolanib or sorafenib (P < 0.05). The crenolanib and sorafenib combination was tolerable without dose-limiting toxicities, and three complete remissions (one with incomplete count recovery) and one partial remission were observed in 8 evaluable patients. Median crenolanib apparent clearance showed a nonsignificant decrease during treatment (45.0, 40.5, and 20.3 L/hour/m2 on days 1, 7, and 14, respectively) without drug–drug interaction. Only 1 patient developed a FLT3-KD mutation (FLT3 F691L).

Conclusions:

The combination of crenolanib and sorafenib was tolerable with antileukemic activities and rare emergence of FLT3-TKD mutations, which warrants further investigation.
Timed Up and Go in People with Subjective Cognitive Decline Is Associated with Faster Cognitive Deterioration and Cortical Thickness
Authors: Borda Miguel Germán , Ferreira Daniel , Selnes Per , Tovar-Rios Diego Alejandro , Jaramillo-Jiménez Alberto , Kirsebom Bjørn-Eivind , Garcia-Cifuentes Elkin , Dalaker Turi O. , Oppedal Ketil , Sønnesyn Hogne , Fladby Tormod , Aarsland Dag
Published: March 2022

Introduction: Early markers of neurodegeneration provide an opportunity to detect, monitor, and initiate interventions in individuals who have an increased risk of developing dementia. Here, we investigated whether the Timed Up and Go (TUG) test is associated with early brain neurodegeneration and whether the TUG test could be a marker of cognitive decline in people with subjective cognitive decline (SCD). Methods: This is a longitudinal analysis of the Dementia Disease Initiation Study, a prospective, community-based, cohort study from Norway, designed to investigate early markers of cognitive impairment and dementia. Participants were classified as SCD and healthy controls (HC). The main studied variables were the TUG test and cognition as measured by the Mini-Mental State Examination and the Consortium to Establish a Registry for Alzheimer’s Disease memory composite score. Additionally, we investigated the cross-sectional association of brain morphology with the TUG using 1.5T-MRI. Results: The sample included 45 participants (SCD = 21, HC = 24) followed during a mean time of 1.50 ± 0.70 years. At baseline, the cognitive performance did not differ between the groups, but TUG was longer in SCD. Slower baseline TUG was associated with a faster cognitive decline in both groups and it was also associated with reduced cortical thickness especially in motor, executive, associative, and somatosensory cortical regions in people with SCD. Discussion/Conclusion: TUG predicted cognitive change in individuals with SCD, and there was a negative association between TUG and cortical thickness. TUG is a promising cheap and noninvasive marker of early cognitive decline and may help initiate interventions in individuals who have an increased risk of dementia.
Oncological Outcomes after Pulmonary Metastasectomy for Head and Neck Squamous-Cell Carcinoma and Adenoid Cystic Carcinoma
Authors: Park Hanaro , Han Sungjun , Jeong Woo-Jin , Jung Young Ho , Sung Myung-Whun , Ahn Soon-Hyun
Published: March 2022

Introduction: Pulmonary metastatic head and neck cancer has a poor prognosis. Pulmonary metastasectomy has been performed but only in carefully selected patients. The aim of this study was to examine the clinical characteristics and oncological follow-up of patients who underwent pulmonary metastasectomy. Methods: Data of 54 patients with squamous-cell carcinoma (SCC) or adenoid cystic carcinoma (ACC) who underwent metastasectomy between 2003 and 2019 at two tertiary referral centers were retrospectively reviewed. Results: The 3-year and 5-year disease-free survival rates after metastasectomy were 49.9% and 39.9% for SCC and 38.9% and 32.4% for ACC, respectively. Univariate analysis revealed that a disease-free interval (DFI) less than 14 months and R1 resection (hazard ratio [HR] [95% confidence interval]: 2.95 [0.77–5.62], 4.64 [0.99–21.65], respectively) were risk factors for recurrence in SCC and that a high T stage (HR: 5.24 [1.22–22.58]) was a risk factor in ACC. In SCC, a DFI less than 14 months and R1 resection (hazard ratio: 6.35 [1.36–29.54], 12.79 [1.53–106.95], respectively) were risk factors in a multivariate analysis. Conclusion: Pulmonary metastasectomy had a fair effect on head and neck SCC and ACC, and the prognosis was better in SCC patients with a DFI greater than 14 months.
Implications of COVID-19 on Thrombotic Profile of Severely Affected Patients
Authors: Srivastava Swati , Garg Iti , Dogra Vikas , Bargotya Mona , Bhattar Sonali , Gupta Utkarsh , Jain Shruti , Hussain Javid , Hembrom Anju A. , Ghosh Nilanjana , Kumar Vinay , Varshney Rajeev , Ganju Lilly
Published: March 2022

Introduction: Coronavirus disease 2019 (COVID-19) is a novel viral disease that spread as a global pandemic in 2020 by infecting millions of people across the world. Its clinical prognosis is dependent on various coagulatory parameters since thrombotic events are frequently associated with infection severity. Methods: A total of 383 COVID-19 patients enrolled in Rajiv Gandhi Super Specialty Hospital, Delhi, India, were included in the present retrospective study. Patients were divided into three categories, severe (n = 141), moderate (n = 138), and mild (n = 104) based on infection severity. Various thrombotic parameters and anticoagulant levels were measured in 70 patients and further analyzed. Results: Coagulopathy is seen in COVID-19 patients (n = 70) with a significant increase in fibrinogen, D-dimer levels, and prothrombin time in patients with severe and moderate disease compared to patients with a mild infection. Approximately, 70% of patients with severe and moderate disease demonstrated fibrinogen levels higher than the standard reference range. 60.41% of patients with severe disease showed significantly higher D-dimer levels. Thrombotic parameters were notably elevated in the nonsurvivors group compared to COVID-19 survivors. Nearly, 91% of patients with severe infection had anticoagulant protein S levels below the reference range. Conclusion: COVID-19 infection severely impacts the blood coagulation cascade, which might lead to the manifestation of severe symptoms and increased mortality in patients.
Intranasal Administration of Extracellular Vesicles Mitigates Apoptosis in a Mouse Model of Neonatal Hypoxic-Ischemic Brain Injury
Authors: Lawson Abby , Snyder William , Peeples Eric S.
Published: March 2022

Introduction: Neonatal hypoxic-ischemic brain injury (HIBI) results in significant morbidity and mortality despite current available therapies. Seeking a potential supplemental therapy for HIBI, we investigated the neuroprotective effects of extracellular vesicles derived from neural stem cells (NSC-EVs) and hypoxia-preconditioned brain cells (brain-EVs). Methods: HIBI was induced in postnatal day 9 mice by carotid ligation followed by hypoxia. Following injury, NSC-EVs, brain-EVs, or saline were administered intranasally. Brains were assessed for infarct size, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining, and caspase-3 expression. Additionally, brain-EV microRNA (miRNA) contents were analyzed by miRNA sequencing. Results: Both EV treated groups showed decreased infarct size (brain-EVs p = 0.004 and NSC-EVs p = 0.052), and although NSC-EV administration resulted in significantly fewer TUNEL+ cells (p = 0.0098), there was no change in caspase-3 expression after NSC-EV administration, suggesting a caspase-3-independent mechanism. Brain-EVs resulted in a nonsignificant decrease in TUNEL+ cells (p = 0.167) but significant decreases in caspase expression (cleaved p = 0.015 and intact p = 0.026). Brain-EVs consistently expressed several miRNAs, including two which have been shown to be downregulated after HIBI: miR-342-3p and miR-330-3p. Conclusion: Understanding the regenerative effects and contents of NSC-EVs and brain-EVs could allow for the development of targeted EV-based therapies that could reduce morbidity and mortality for neonates affected by HIBI.
Methods of Assessing Frailty in the Critically Ill: A Systematic Review of the Current Literature
Authors: Bertschi Daniela , Waskowski Jan , Schilling Manuel , Donatsch Claudia , Schefold Joerg Christian , Pfortmueller Carmen Andrea
Published: March 2022

Introduction: As new treatments have become established, more frail pre-ICU patients are being admitted to intensive care units (ICUs); this is creating new challenges to provide adequate care and to ensure that resources are allocated in an ethical and economical manner. This systematic review evaluates the current standard for assessing frailty on the ICU, including methods of assessment, time point of measurements, and cut-offs. Methods: A systematic search was conducted on MEDLINE, Clinical Trials, Cochrane Library, and Embase. Randomized and non-randomized controlled studies were included that evaluated diagnostic tools and ICU outcomes for frailty. Exclusion criteria were the following: studies without baseline assessment of frailty on ICU admission, studies in paediatric patients or pregnant women, and studies that targeted very narrow populations of ICU patients. Eligible articles were included until January 31, 2021. Methodological quality was assessed using the Newcastle-Ottawa Scale. No meta-analysis was performed, due to heterogeneity. Results: N = 57 articles (253,376 patients) were included using 19 different methods to assess frailty or a surrogate. Frailty on ICU admission was most frequently detected using the Clinical Frailty Scale (CFS) (n = 35, 60.3%), the Frailty Index (n = 5, 8.6%), and Fried’s frailty phenotype (n = 6, 10.3%). N = 22 (37.9%) studies assessed functional status. Cut-offs, time points, and manner of baseline assessment of frailty on ICU admission varied widely. Frailty on ICU admission was associated with short- and long-term mortality, functional and cognitive impairment, increased health care dependency, and impaired quality of life post-ICU discharge. Conclusions: Frailty assessment on the ICU is heterogeneous with respect to methods, cut-offs, and time points. The CFS may best reflect frailty in the ICU. Frailty assessments should be harmonized and performed routinely in the critically ill.
Changes in Health Expectancy for Older Adults in Japan: Results from Two Longitudinal Surveys
Authors: Minagawa Yuka , Saito Yasuhiko
Published: March 2022

Background: Although there is evidence of improvements in health expectancy among the Japanese population, existing estimates are based on the prevalence of morbidity taken from cross-sectional survey data. Objectives: This study employed a multistate life table approach to compute incidence-based health expectancy measures, namely active and inactive life expectancy (LE), for two longitudinal survey cohorts from 1990 to 2009. Methods: This study used data from two longitudinal surveys of older adults in Japan: the National Survey of the Japanese Elderly (1990–1999) and the Nihon University Japanese Longitudinal Study of Aging (1999–2009). We employed the Interpolation of Markov Chains technique to compute the number of years at age 65 years to be spent with and without difficulty in performing activities of daily living (ADLs) or instrumental ADLs. Results: We documented significant increases in active LE for men and women at age 65 years over the 10-year study period. There were significant changes in total LE for men and in the percentage of active life for women. Inactive LE did not significantly change across the two survey cohorts during the study period. Conclusions: Our results show that the health status of older adults in Japan has largely improved over the 10-year study period, with increases in both the duration of life and time spent in an active state. This finding offers evidence of compression of morbidity among older men and women in Japan.
Immunodepression, Infections, and Functional Outcome in Ischemic Stroke
Authors: Westendorp Willeke F. , Dames Claudia , Nederkoorn Paul J. , Meisel Andreas
Published: March 2022

Stroke remains one of the main causes of mortality and morbidity worldwide. Immediately after stroke, a neuroinflammatory process starts in the brain, triggering a systemic immunodepression mainly through excessive activation of the autonomous nervous system. Manifestations of immunodepression include lymphopenia but also dysfunctional innate and adaptive immune cells. The resulting impaired antibacterial defenses render patients with stroke susceptible to infections. In addition, other risk factors like stroke severity, dysphagia, impaired consciousness, mechanical ventilation, catheterization, and older age predispose stroke patients for infections. Most common infections are pneumonia and urinary tract infection, both occur in ≈10% of the patients. Especially pneumonia increases unfavorable outcome and mortality in patients with stroke; systemic effects like hypotension, fever, delay in rehabilitation are thought to play a crucial role. Experimental and clinical data suggest that systemic infections enhance autoreactive immune responses against brain antigens and thus negatively affect outcome but convincing evidence is lacking. Prevention of poststroke infections by preventive antibiotic therapy did not improve functional outcome after stroke. Immunomodulatory approaches counteracting immunodepression to prevent stroke-associated pneumonia need to account for neuroinflammation in the ischemic brain and avoid further tissue damage. Experimental studies discovered interesting targets, but these have not yet been investigated in patients with stroke. A better understanding of the pathobiology may help to develop optimized approaches of preventive antibiotic therapy or immunomodulation to effectively prevent stroke-associated pneumonia while improving long-term outcome after stroke. In this review, we aim to characterize epidemiology, risk factors, cause, diagnosis, clinical presentation, and potential treatment of poststroke immunosuppression and associated infections.
eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems
Authors: Vecchi Edoardo , Pospíšil Lukáš , Albrecht Steffen , O'Kane Terence J. , Horenko Illia
Published: March 2022

Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA). Specifically, we propose to change the order of the optimization steps and replace the most computationally expensive subproblem of eSPA with its closed-form solution. We prove that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms. On several small data learning benchmarks, we show that the eSPA+ algorithm achieves a many-fold speed-up with respect to eSPA and even better performance results when compared to a wide array of ML and DL tools. In particular, we benchmark eSPA+ against the standard eSPA and the main classes of common learning algorithms in the small data regime: various forms of support vector machines, random forests, and long short-term memory algorithms. In all the considered applications, the common learning methods and eSPA are markedly outperformed by eSPA+, which achieves significantly higher prediction accuracy with an orders-of-magnitude lower computational cost.
A new species of the Colocasiomyia gigantea species group (Diptera, Drosophilidae) breeding on inflorescences of Scindapsus maclurei (Araceae)
Authors: XUE XIAN-CI , JIAO RUN-JIE , YU LI , GAO JIAN-JUN
Published: March 2022

The gigantea group is one of the six species groups of the genus Colocasiomyia Meijere, 1914 (Diptera, Drosophilidae). All the nine known species of this group breed on inflorescence/infructescence of host plants of the subfamily Monsteroideae (Araceae) and are geographically restricted to the Oriental region: seven species found exclusively from Rhaphidophora spp. in southern China, and the remaining two from Sabah (host plant: Scindapsus coriaceus) in Borneo or from West Java (host plant: Epipremnum pinnatum). In the present paper, a new member of the gigantea group, C. daiae sp. nov., is described, with adult specimens and eggs collected from inflorescences of Scindapsus maclurei in Hainan, China and larvae and pupae reared from field-collected eggs in the laboratory.