Correction Technique Research Articles

Impact of surgical technique on postoperative ventricular arrhythmias in arrhythmic mitral valve prolapse

Abstract Background Mitral valve prolapse (MVP) is associated with progressive mitral regurgitation (MR) requiring surgical correction. A subset of patients with MVP may also constitute an "arrhythmic" phenotype suffering from malignant ventricular arrhythmias (VAs). Purpose To assess the impact of surgical technique for correction of degenerative MR including repair using artificial chords, repair with resection and mitral valve replacement (MVR) on longitudinal risk of VAs in patients with arrhythmic MVP. Methods Patients who had undergone mitral valve surgery for MVP with moderate to severe degenerative MR between 2010-2022 at a university hospital in Sweden were retrospectively included. Patients with ischemic heart disease, primary cardiomyopathy, channelopathy, or endocarditis were excluded. To examine changes in VAs (defined as sustained- or non-sustained ventricular tachycardias) as assessed by continuous Holter monitoring before and after surgery, generalized estimating equation (GEE) models were performed to account for the correlations between repeated monitoring in each patient and for differences in the time of measurement. Results Of 599 patients undergoing mitral valve surgery, 423 (71%) underwent repair using artificial chords, 62 (10%) repair with resection and 114 (19%) MVR. For those presenting with symptoms such as palpitations, presyncope, and/or syncope, Holter monitoring was conducted before (n=171; 29%) and/or after mitral valve surgery (n=184; 31%) (Figure 1). The distribution of VAs before and after surgery, stratified by surgical technique, is illustrated in Figure 2. In GEE models, repair using artificial chords was associated with increased postoperative VAs compared with the other surgical techniques (OR 2.14; 95% CI 1.19-3.83; p=0.01), repair with resection with reduced postoperative VAs compared with the other surgical techniques (OR 0.15; 95% CI 0.04-0.61; p=0.01), and no change in postoperative VAs was observed for MVR compared with the other surgical techniques (OR 0.724; 95% CI 0.39-1.35; p=0.31). The results remained similar after adjustment for age, sex, myxomatous disease/fibroelastic deficiency or bileaflet prolapse. Conclusion In patients with symptoms of palpitations, presyncope and/or syncope before or after mitral valve surgery, repair using artificial chords was associated with an increased risk of postoperative VAs. Prospective studies including continuous monitoring before and after surgery are needed to confirm these findings.

Impact of Cavity Length Non-uniformity on Reaction Rate Extraction in Strong Coupling Experiments.

Reports of altered chemical phenomena under vibrational strong coupling, including reaction rates, product distributions, intermolecular forces, and cavity-mediated vibrational energy transfer, have been met with a great deal of skepticism due to several irreproducible results and the lack of an accepted theoretical framework. In this work, we add some insight by identifying a UV-vis measurement artifact that distorts observed absorption peak positions, amplitudes, and consequently, chemical reaction rates extracted in optical microcavities. We predict and characterize the behavior of this artifact using the Transfer Matrix (TM) method and confirm its presence experimentally. We then present a correction technique whereby an effective molar absorption coefficient is assigned to an absorbing species within the cavity. These revelations have important implications for many existing examples of cavity-modified chemistry and establishing best practices for carrying out robust future investigations.

"Folding Fan Flap": A Novel and Dependable Surgical Technique for Crow's Feet Correction by Z-Plasty of Orbicularis Oculi Muscle.

The challenge of crow's feet surgical correction is to minimize the complication caused by orbicularis oculi muscle (OOM) resection while ensuring the operative effect. This study aims to propose a novel technique to solve this problem and evaluate its efficacy. From January 2022 to April 2024, all patients who received "Folding fan flap" in our institution were included. The outer third of the OOM below the lateral canthus was dissected and received Z-plasty to exchange width for length and reconstruct the muscle dynamics in a non-circular direction. Preoperative and postoperative smiling crow's feet were assessed by two independent physicians using the Lemperle wrinkle scale. At least six months after surgery, patient satisfaction with wrinkle correction, naturalness of facial expression, and local flattening was subjectively evaluated on a five-point scale. Complications including hematomas, incomplete eyelid closure, facial stiffness, and forehead numbness were recorded. A total of 32 patients (63 sides) were included with a mean follow-up of 13.9 ± 5.2 months. Postoperative assessments showed a significant reduction in Lemperle's score from 4.8 ± 0.4 to 1.5 ± 0.5 (P=0.000). Regarding the satisfaction of surgical outcomes and the naturalness of facial expressions, thirty patients (93.8%) were very satisfied. There were no severe complications such as hematomas and incomplete eyelid closure. Preliminary clinical studies demonstrated the efficacy of this technique in ensuring outcomes while preserving normal dynamic expressions. By Z-plasty of the outer circle of OOM, the muscle dynamic was successfully modified rather than eradicated. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours

Objective.Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach.The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5 T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results.The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/126) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance.We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.

FERDCNN: an efficient method for facial expression recognition through deep convolutional neural networks

Facial expression recognition (FER) has caught the research community’s attention recently because it can affect many real-life applications. Multiple studies have focused on automatic FER, most of which use a machine learning methodology, FER has continued to be a difficult and exciting issue in computer vision. Deep learning has recently drawn increased attention as a solution to several practical issues, including facial expression recognition. This article introduces an efficient method for FER (FERDCNN) verified on five different pre-trained deep CNN (DCNN) models (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101). In the proposed method, firstly the input image has been pre-processed using face detection, resizing, gamma correction, and histogram equalization techniques. Secondly, the images go through DCNN to extract deep features. Finally, support vector machine (SVM) and transfer learning are used to classify generated features. Recent methods have been employed to evaluate and contrast the performance of the proposed approach on two publicly standard databases namely, CK+ and JAFFE on the seven classes of fundamental emotions, including anger, disgust, fear, happiness, sadness, and surprise beside neutrality for CK+ and contempt for JAFFE. The suggested method tested Four different traditional supervised classifiers with deep features, Experimental found that AlexNet excels as a feature extractor, while SVM demonstrates superiority as a classifier because of this combination achieving the highest accuracy rates of 99.0% and 95.16% for the CK+ database and the JAFFE datasets, respectively.

Catheter Exchange With Elongation of Tunnel (CEET) Procedure-A Novel Technique for Cuff Extrusion of Tunneled Dialysis Catheter: Surgical Experience and Early Outcomes.

Cuff extrusion of tunneled dialysis catheter (TDC) leads to catheter dysfunction, leading to loss of vascular access and the need for new catheter. Definitive management is to remove TDC and reinsert new catheter by new venous puncture and tunnel, which may not be possible in all cases. The study evaluated the surgical experience and early outcomes of a novel "Catheter Exchange with Elongation of Tunnel (CEET)" procedure for cuff extrusion. The retrospective study included all cases of hemodialysis with TDC with partial or complete cuff extrusion and excluded complete catheter dislodgement, tunnel infection, or any catheter related infection. All patients also underwent the CEET procedure under fluoroscopy guidance, and the clinical details and outcomes were analyzed. Eleven cases of TDC cuff extrusion underwent the CEET procedure of which three (27.2%) had previous and four (36.4%) had partial cuff extrusion, and seven cases (63.6%) had short tunnel length, which likely predisposed to cuff extrusion. CEET procedure was successful in 10 cases (success rate 90.1%) with desired position of catheter tip and good blood flow. Study population was divided into early and late cuff extrusion (≥1 month). Short tunnel length was associated with late extrusion (p = 0.05), whereas premature removal of TDC anchor sutures was associated with early cuff extrusion (p = 0.04). CEET procedure is a successful alternative technique for correction of cuff extrusion of TDC with good success rate. Premature removal of anchor sutures was associated with early cuff extrusion, whereas short tunnel length was associated with late cuff extrusion.

The effect of posture regulation training on musculoskeletal disorders, fatigue level and job performance in intensive care nurses

BackgroundIntensive care units (ICUs) are one of the high-risk working areas in terms of musculoskeletal disability and ergonomic risks including the environment and posture factors. Correct posture technique is often ignored by nurses working in these units. This study was conducted to determine the effect of posture regulation training on work-related musculoskeletal disorders, fatigue level and job performance in nurses working in ICUs.MethodsThis quasi-experimental study with one-group pretest-posttest design included 64 intensive care nurses. The nurses received posture regulation training in three different sessions. The post-test was administered four months after the posture regulation training.ResultsThe nurses reported to frequently have aches, pains and discomfort in the neck, upper back and lumbar regions. After the posture regulation training, their level of pain, ache and discomfort in the neck, right and left shoulder, upper back, lower back and right/left foot areas decreased significantly (p < 0.05). The posture regulation training reduced the levels of behavior/severity and affect, which are sub-dimensions of fatigue, and increased the level of contribution to work, which is a sub-dimension of job performance (p < 0.05).ConclusionThe posture regulation training decreased the level of symptoms in the neck, shoulder, upper back, lower back and foot regions of intensive care nurses and partially improved their fatigue level and job performance. Therefore, posture regulation training should be added to in-service training programs and permanent measures should be taken for ergonomic risks in ICUs.

Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p &lt; 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures.

Surgical treatment algorithm for thoracic and lumbar hyperkyphosis in pediatric population.

There is no standardized and universally accepted surgical treatment for thoracic and lumbar hyperkyphosis in children. A surgical treatment algorithm was developed to aid in the choice of the appropriate corrective technique. A retrospective analysis was conducted of patients younger than 18years who underwent primary correction surgery and posterior fusion for thoracic and lumbar hyperkyphosis. Patients were categorized according to the classification of Rajasekaran et al. and divided into 2 groups: a posterior column osteotomies (PCO) group and a three-column osteotomies (3CO) group. We analyzed the angularity and flexibility of the curve, global sagittal balance, and preoperative neurological status of the patient. Forty-nine patients were included. The mean age was 12.8years (1-18) and the mean preoperative kyphosis was 93.5° (40°-175°). In the PCO group (N = 30), the sagittal deformity angular ratio (DAR) was less than 16.5 in 26 patients (86.6%), while in the 3CO group (N = 19), sagittal DAR was greater than 16.5 in 17 patients (89.4%). In the PCO group (N = 28), the T1-Pelvic angle (TPA) was less than 17° in 19 patients (67.8%), while in the 3CO group (N = 15), TPA was greater than 17° in 10 patients (66.6%). Five patients (10.2%) had recent-onset or progressive neurological alteration, all of them (100%) required 3CO. An algorithm for kyphosis was developed based on the classification by Rajasekaran et al., preoperative analysis of the angularity and flexibility of the curve, global sagittal balance and neurological status of the patient, to aid in the choice of the appropriate corrective osteotomy. IV.

A deep convolutional neural network for blind element error correction of spatial heterodyne spectrometer using line selective convolutional blocks

The "GF Special Project" is a massive remote sensing technology initiative including a number of satellites and various observation platforms. GF-5 is the satellite with the most payloads, the highest spectral resolution, and the most difficulty in development, and it can monitor a variety of environmental elements using spatial heterodyne spectroscopy (SHS) technology, including atmospheric aerosols, carbon dioxide, methane, terrestrial vegetation, straw burning, and urban heat islands. In this study, a novel blind element error correction technique based on deep learning network is investigated and developed for spatial heterodyne interferograms, as well as the formation mechanism and distribution characteristics of the SHS interferometric data. LSConv-Net, a new CNN model, was created and trained to denoise in the presence of high-density and ultra-high-density blind element errors. We do this by introducing a new line-selective convolutional (LSConv) block. Simultaneously, experimental validation of blind element error correction utilizing laboratory water vapor interferometric data and atmospheric CO2 absorption interferometric data from GF-5, and the change in FWHM before and after the experiment was tested using potassium lamp interferograms. Experiments show that the Deep neural networks trained with this model may successfully suppress the effect of blind element noise on spectra, recover spectra that have been overwhelmed by high-density blind element noise without any effect on other non-blind pixels, and surpass all similar techniques in terms of spectral recovery.

Diagnostic and treatment pitfalls in glossopharyngeal neuralgia: evidence from a case series.

Glossopharyngeal neuralgia (GN) is a rare pain syndrome often underdiagnosed. This study aimed to assess the challenges in diagnosing GN and identify patients at risk of misdiagnosis. Between 2010 and 2019, nine patients underwent microvascular decompression (MVD) of the glossopharyngeal nerve at two tertiary care hospitals. A retrospective analysis examined symptom characteristics, time to correct diagnosis, operative technique, and clinical outcomes. Barrow Neurological Institute (BNI) pain scores were assessed before surgery and on follow-up. Six women and three men, aged 55 ± 14.3years, with unilateral GN were included. Pain was predominantly in the ear (otalgic type) in four patients (44.4%), the pharynx or base of the tongue (pharyngeal type) in four (44.4%), and mixed in one (11.1%). Five patients reported pain radiating to the mandible, all initially misdiagnosed with trigeminal neuralgia (TN) and treated for 2.4(± 3.1) years before presenting to our institution. One case resulted in MVD of the trigeminal nerve and subsequent thermocoagulation of the Gasserian ganglion due to persistent pain. The correct diagnosis of GN was established later in patients with otalgic or mixed pain (3.8 ± 3.4years) compared to those with pharyngeal pain (0.5 ± 1.0years), showing a trend (U = 17.0, p = 0.07). There was a significant association between pain radiation to the mandible and misdiagnosis (x2 = 9.00, p = 0.003). Endoscopically assisted MVD via the median suboccipital subtonsillary approach was performed, resolving neurovascular conflicts with the PICA (9/9) and vertebral artery (3/9). Follow-up after 15.7(± 18.2) months showed significant pain reduction according to the BNI (preoperative BNI = 5.0 ± 0.0, postoperative BNI = 1.7 ± 1.0,t (8) = 10,CI [1.6-5.1], p < 0.001). No permanent surgery-related deficits occurred. Patients with GN, especially those with predominant otalgic pain and pain radiating to the mandible, are at higher risk of misdiagnosis due to similarities with TN. Despite the rarity of GN, increased awareness of its various pain manifestations may lead to earlier correct diagnoses, which is crucial for surgical treatment.

Research on Precise Attitude Measurement Technology for Satellite Extension Booms Based on the Star Tracker

This paper reports the successful application of a self-developed, miniaturized, low-power nano-star tracker for precise attitude measurement of a 5-m-long satellite extension boom. Such extension booms are widely used in space science missions to extend and support payloads like magnetometers. The nano-star tracker, based on a CMOS image sensor, weighs 150 g (including the baffle), has a total power consumption of approximately 0.85 W, and achieves a pointing accuracy of about 5 arcseconds. It is paired with a low-cost, commercial lens and utilizes automated calibration techniques for measurement correction of the collected data. This system has been successfully applied to the precise attitude measurement of the 5-m magnetometer boom on the Chinese Advanced Space Technology Demonstration Satellite (SATech-01). Analysis of the in-orbit measurement data shows that within shadowed regions, the extension boom remains stable relative to the satellite, with a standard deviation of 30′′ (1σ). The average Euler angles for the “X-Y-Z” rotation sequence from the extension boom to the satellite are [−89.49°, 0.08°, 90.11°]. In the transition zone from shadow to sunlight, influenced by vibrations and thermal factors during satellite attitude adjustments, the maximum angular fluctuation of the extension boom relative to the satellite is approximately ±2°. These data and the accuracy of the measurements can effectively correct magnetic field vector measurements.

Dynamic correction of forest fire spread prediction using observation error covariance matrix estimation technique based on FLC-GRU

BackgroundData assimilation (DA) techniques have played a significant role in improving the prediction accuracy of forest fire spread. The dynamic correction technique weights the predicted and observed values to obtain an analytical value that better reflects the position of the fire perimeter. The weighted importance of each contribution is determined by the magnitude of its associated error. However, as a crucial parameter affecting prediction accuracy, the covariance matrix of observation errors is often inaccurate and neglects its own temporal correlation. This is unfriendly to spread prediction results. To address this issue, we proposed a targeted technique for estimating the observation error covariance matrix (R matrix) based on the Fire Line Convolutional Gated Recurrent Unit (FLC-GRU).ResultsWe integrated this method into the DA framework and validated its applicability and accuracy using Observing System Simulation Experiment (OSSE). Through comparisons with traditional methods, the results indicate that using the FLC-GRU estimated R matrix for correction calculations leads to wildfire prediction locations that are closer to the true values.ConclusionsThe proposed approach learns the covariance matrix directly from time-series observed fire line data, without requiring any prior knowledge or assumptions about the error distribution, in contrast to classical posterior tuning methods. The proposed method significantly improves the rationality and accuracy of R matrix estimation, enhances the utility of observational data, and thereby improves the correction accuracy of forest fire spread predictions. Moreover, the study also demonstrates the applicability of the proposed method within the DA framework.

Toward a resource-optimized dynamic quantum algorithm via non-iterative auxiliary subspace corrections.

Recent quantum algorithms pertaining to electronic structure theory primarily focus on the threshold-based dynamic construction of ansatz by selectively including important many-body operators. These methods can be made systematically more accurate by tuning the threshold to include a greater number of operators into the ansatz. However, such improvements come at the cost of rapid proliferation of the circuit depth, especially for highly correlated molecular systems. In this work, we address this issue by the development of a novel theoretical framework that relies on the segregation of an ansatz into a dynamically selected core "principal" component, which is, by construction, adiabatically decoupled from the remaining operators. This enables us to perform computations involving the principal component using extremely shallow-depth circuits, whereas the effect of the remaining "auxiliary" component is folded into the energy function via a cost-efficient non-iterative correction, ensuring the requisite accuracy. We propose a formalism that analytically predicts the auxiliary parameters from the principal ones, followed by a suite of non-iterative auxiliary subspace correction techniques with different levels of sophistication. The auxiliary subspace corrections incur no additional quantum resources yet complement an inadequately expressive core of the ansatz to recover a significant amount of electronic correlations. We have numerically validated the resource efficiency and accuracy of our formalism with a number of strongly correlated molecular systems.

A Study on Enhancing the Visual Fidelity of Aviation Simulators Using WGAN-GP for Remote Sensing Image Color Correction

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force.

Multi-bit error detection and correction technique using HVDK (Horizontal-Vertical-Diagonal-Knight) parity

In a data stream, errors are quite likely to occur and sometimes this is much more terrible. So, data safety is very important in digital systems, especially in critical and real-time systems, microprocessors, embedded systems, computer memory, and data communication. The probability of soft error increases with the exponential rate of increasing transistor per chip, operational voltage, particle strike, condensation of bit-cell area, etc. To ensure data integrity, safety, and system reliability, error detection, and correction are fundamental components of data transmission and storage systems. Existing error correction techniques can solve several bits of error. However, these existing methods are not fully efficient, as some consume a lot of time, space, and bit overhead. An ideal approach will have the potential to minimize all of these parameters. This research paper proposes a novel error correction approach with horizontal, vertical, diagonal, and knight (HVDK) parity bits. This approach has been taken to correct 5-bit errors in 64 bits of data word using the parity-based technique with less bit overhead. Our research advances the knowledge of error correction methods and sheds light on how to pick and use parity bit schemes that are appropriate for different applications.

Формирование двигательных действий у детей с расстройствами аутистического спектра на занятиях адаптивной физической культурой

&lt;p style="text-align: justify;"&gt;&lt;strong&gt;Objectives. &lt;/strong&gt;Ski training classes for children with autism spectrum disorders (ASD) help develop locomotor functions, develop physical qualities, strengthen health and compensate for the lack of motor activity. Adaptive physical education (APE) classes develop basic types of movements, including when moving on skis. Currently, in the practice of APE there are no methods that form motor actions in children with ASD by means of ski training. The author&amp;rsquo;s method of step-by-step formation of motor actions in ski training for children with ASD allows teaching the ability to move on skis in various ways.&lt;/p&gt; &lt;p style="text-align: justify;"&gt;&lt;strong&gt;Methods&lt;/strong&gt;. The article presents the materials of an empirical study conducted in a group of 6 children with ASD aged 8&amp;mdash;9 years. When teaching children motor skills in APE classes, the method of step-by-step formation of motor actions by means of ski training was used. Motor skills were assessed by the tests &amp;ldquo;Sliding step&amp;rdquo;, &amp;ldquo;Alternating two-step move&amp;rdquo;, &amp;ldquo;Simultaneous stepless move&amp;rdquo;. Diagnostics of the technique of performing ski moves was carried out by the method of expert assessment.&lt;/p&gt; &lt;p style="text-align: justify;"&gt;&lt;strong&gt;Results&lt;/strong&gt;. The sliding step was mastered most successfully by children: 83% (n=5) of children mastered the sliding step technique. The correct technique for performing a simultaneous stepless and alternating two-step walk was mastered by 67% of children (n=4). The number of children capable of covering the distance using the alternating two-step walk and simultaneous stepless walk techniques increased from 1 to 5.&lt;/p&gt; &lt;p style="text-align: justify;"&gt;&lt;strong&gt;Conclusions.&amp;nbsp;&lt;/strong&gt;The presented results confirm the possibility of children with ASD mastering certain methods of skiing using a step-by-step method of forming motor actions. Application at each stage of non-verbal techniques, teaching methods (for example, action by demonstration), form the technique of ski moves in children. The proposed method was implemented in a guideline for the step-by-step teaching of motor actions in ski training for children with ASD. In the future, it is planned to develop a guideline for teaching the techniques of turns, ascents, and descents in ski training for children with ASD.&lt;/p&gt;

Fusion of Color Correction and HSV Segmentation Techniques for Automated Segmentation of Acute Lymphoblastic Leukemia.

This article presents an enhanced segmentation methodology for the accurate detection of acute lymphoblastic leukemia (ALL) in blood smear images. The proposed approach integrates color correction techniques with HSV color space segmentation to improve white blood cell analysis. Our method addresses common challenges in microscopic image processing, including sensor nonlinearity, uneven illumination, and color distortions. The key objectives of this study are to develop a robust preprocessing pipeline that normalizes blood smear images for consistent analysis, implement an HSV-based segmentation technique optimized for leukocyte detection, and validate the method's effectiveness across various ALL subtypes using clinical samples. The proposed technique was evaluated using real-world blood smear samples from ALL patients. Quantitative analysis demonstrates significant improvements in segmentation accuracy compared to traditional methods. Our approach shows strong capability in reliably detecting and segmenting ALL subtypes, offering the potential for enhanced diagnostic support in clinical settings.

Bilateral transobturator hysterosuspension using titanium tape-shaped mesh implants in women with apical prolapse of the internal genitalia

BACKGROUND: Pelvic organ prolapse is a common pathology with several relapses of the disease after surgical treatment using exclusively self-tissues, which necessitates the development of new surgical techniques using durable and biological inert materials. AIM: To evaluate the effectiveness and safety of an original surgical technique for apical prolapse correction in patients of reproductive and pre- and postmenopausal age. MATERIAL AND METHODS: Twenty-nine patients were examined and operated on using a developed original technique with the use of narrow tape-shaped titanium mesh implants. RESULTS: Follow-up was conducted at 1, 6, 12, and 24 months postoperatively. Comparison of patient survey indicators in the pre- and postoperative periods showed an improvement in the patients’ quality of life and an increase in their social and sexual activity. During the first month, 15 (51.7%) patients reported intermittent pulling pains in the perineal and/or inguinal–femoral folds, and 4 (13.8%) reported frequent urination. Clinical examination revealed complete preservation of the surgical correction of prolapse in all patients. Gynecological and rectal examinations and transperineal and transvaginal echography did not show displacement of pelvic organs or titanium implants. Mesh-associated complications were not noted during the observation period. CONCLUSION: The developed original technique for surgical correction of second-degree apical prolapse with the use of narrow ribbon-shaped titanium mesh implants is effective and safe. This is evidenced by the normalization of the uterus position in the pelvis, absence of displacement of pelvic organs in the near and distant postoperative periods, and absence of mesh-associated complications during the observation time.

A fundus vessel segmentation method based on double skip connections combined with deep supervision.

Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice. This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase. Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.