Mixed Features Research Articles

Prediction of the Structural, Mechanical, and Physical Properties of GaC: As a Potential Third-Generation Semiconductor Material.

Similar to GaN and SiC semiconductors, GaC may be a potential semiconductor because of the mixed elemental features of Ga and C. Unfortunately, the phase stability and mechanical and physical properties of GaC are unknown. To search for novel third-generation semiconductors, the present study delves into an in-depth analysis of the structural stability and electronic and optical properties of GaC by DFT calculations. Similar to GaN, three GaC phases are discussed. It is found that three GaC phases (two cubic phases and one hexagonal phase) are first predicted. The band gaps of Fm3̅m, F4̅3m, and hexagonal GaC are 0.449, 2.733, and 3.340 eV, respectively. In particular, the band gap of F4̅3m GaC and hexagonal GaC is bigger than that of GaN. Compared to GaN semiconductors, the C-2p state is in the valence band region across the Fermi level, which is beneficial to electronic mobility and electronic transport capacity near the Fermi level. In addition, three GaC phases exhibit ultraviolet properties. The first peak of three GaC phases produces the right migration compared to the GaN semiconductor. Therefore, the author predicts that GaC is a potential third-generation semiconductor material which is potentially used in future third-generation semiconductor industries.

Cariprazine as a maintenance therapy in the prevention of mood episodes in adults with bipolar I disorder.

Cariprazine treats acute manic and depressive episodes in bipolar I disorder (BP-I), but its efficacy in preventing relapse of mood episode remains unknown. In this phase 3b, double-blind, placebo-controlled study, patients with BP-I with acute manic or depressive episodes (each with/without mixed features), were treated with cariprazine 3.0 mg/day during a 16-week open-label treatment period; those who achieved stable remission within 8 weeks and remained stable for at least another 8 weeks were randomized to receive cariprazine 1.5 or 3.0 mg per day or placebo in the double-blind treatment period for up to 39 weeks. The primary efficacy endpoint was time to relapse of any mood episode. Adverse events (AEs) were assessed. Patients (440/896) enrolled in the open-label treatment period achieved stability criteria and were randomized to receive cariprazine 3.0 mg/day (n = 148), cariprazine 1.5 mg/day (n = 147), or placebo (n = 145) in the double-blind treatment period. Relapse rates were 17.9%, 16.8%, and 19.7% in the cariprazine 3.0 mg/day, cariprazine 1.5 mg/day, and placebo groups, respectively. Neither dose of cariprazine was more effective than placebo on the primary outcome (3.0 mg/day: HR = 0.89, [95% CI: 0.5, 1.5]; 1.5 mg/day: HR = 0.83, 95% CI [0.5, 1.4]). The most frequently reported AEs (≥5%) were akathisia, headache, insomnia, and nausea in the open-label treatment period and increased weight and insomnia in the double-blind treatment period. In the open-label and double-blind treatment periods, 7.5% and 1.6% of patients experienced an AE leading to discontinuation. Cariprazine was not superior to placebo in the prevention of relapses in this study. Relapse rates were unusually low in the placebo group. Cariprazine was well-tolerated.

Postresidency Practice Setting and Clinical Care Features According to 3 Versus 4 Years of Training in Family Medicine: A Length of Training Pilot Study.

Factors associated with physician practice choice include residency location, training experiences, and financial incentives. How length of training affects practice setting and clinical care features postgraduation is unknown. In this Length of Training Pilot (LoTP) study, we surveyed 366 graduates of 3-year (3YR) and 434 graduates of 4-year (4YR) programs 1 year after completion of training between 2013 and 2021. Variables assessed included reasons for practice setting choice, practice type, location, practice and community size, specialty mix, and clinical care delivery features (eg, integrated behavioral health, risk stratified care management). We compared different length of training models using χ2 or Fisher's exact tests for categorical variables and independent samples, and t test (unequal variances) for continuous variables. Response rates ranged from 50% to 88% for 3YR graduates and 68% to 95% for 4YR graduates. Scope of practice was a predominant reason for graduates choosing their eventual practice, and salary was a less likely reason for those completing 4 years versus 3 years of training (scope, 72% vs 55%, P=.001; salary, 15% vs 22%, P=.028). Community size, practice size, practice type, specialty mix, and practice in a federally designated underserved site did not differ between the two groups. We found no differences in patient-centered medical home features when comparing the practices of 3YR to 4YR graduates. Training length did not affect practice setting or practice features for graduates of LoTP programs. Future LoTP analyses will examine how length of training affects scope of practice and clinical preparedness, which may elucidate other elements associated with practice choice.

Deep causal learning for pancreatic cancer segmentation in CT sequences

Segmenting the irregular pancreas and inconspicuous tumor simultaneously is an essential but challenging step in diagnosing pancreatic cancer. Current deep-learning (DL) methods usually segment the pancreas or tumor independently using mixed image features, which are disrupted by surrounding complex and low-contrast background tissues. Here, we proposed a deep causal learning framework named CausegNet for pancreas and tumor co-segmentation in 3D CT sequences. Specifically, a causality-aware module and a counterfactual loss are employed to enhance the DL network's comprehension of the anatomical causal relationship between the foreground elements (pancreas and tumor) and the background. By integrating causality into CausegNet, the network focuses solely on extracting intrinsic foreground causal features while effectively learning the potential causality between the pancreas and the tumor. Then based on the extracted causal features, CausegNet applies a counterfactual inference to significantly reduce the background interference and sequentially search for pancreas and tumor from the foreground. Consequently, our approach can handle deformable pancreas and obscure tumors, resulting in superior co-segmentation performance in both public and real clinical datasets, achieving the highest pancreas/tumor Dice coefficients of 86.67%/84.28%. The visualized features and anti-noise experiments further demonstrate the causal interpretability and stability of our method. Furthermore, our approach improves the accuracy and sensitivity of downstream pancreatic cancer risk assessment task by 12.50% and 50.00%, respectively, compared to experienced clinicians, indicating promising clinical applications.

Factors associated with anxious distress in major depressive episodes: a cross-sectional study

IntroductionThe comorbidity between depression and anxiety is a common occurrence. The DSM-5 introduced the “anxious distress” (AD) specifier that can be applied to any depressive episode – both in major depressive disorder (MDD) and bipolar disorder (BD) – when symptoms such as feelings of tension, restlessness, difficulty concentrating, and fear that something awful may happen or to lose control are present. Longitudinal data showed that the AD specifier may be an effective predictor of chronicity, time to remission, and functional disability in depressive disorders. In addition, evidence on AD proved its association with increased depressive symptom severity.ObjectivesAvailable literature seems to suggest that AD occurs in a specific subgroup of patients, thus enabling a peculiar clinical profile to be outlined. To expand knowledge in this field, we performed a cross-sectional study aimed at identifying clinical correlates of AD in people with major depressive episodes.MethodsAdult people admitted to two psychiatric inpatient units in the northern area of the Metropolitan City of Milan from May 2020 to December 2022 were screened for a major depressive episode and relevant specifiers using the Structured Clinical Interview for DSM-5 (SCID-5). Data on socio-demographic and clinical variables were collected. The severity of depressive and manic symptoms was assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS), respectively. Univariate comparisons between participants with and without AD were conducted, and two multiple logistic regression models were arranged to investigate the association between AD and candidate explanatory variables.ResultsWe included 206 inpatients with a major depressive episode (mean age =48.4 ± 18.6 years; males = 38.8%), of whom 155 diagnosed with MDD and 51 with BD. AD was present in 137 participants (66.5%). Mixed features (p=0.049), higher YMRS scores (p=0.004), psychotic features (p<0.05), and a diagnosis of MDD (p<0.05) were found to be associated with AD in the multiple logistic regression analysis.ConclusionsNotwithstanding some limitations, such as the cross-sectional design and the inclusion of inpatient only, our study highlights the association of AD with mixed and psychotic features, as well as with MDD. Clinical implications of these results include the possible contribution in delineating a specific symptom profile in people with AD during a major depressive episode.Disclosure of InterestNone Declared

How Many Criteria Should be Required to Define the DSM-5 Mixed Features Specifier in Depressed Patients?

IntroductionDuring the past 2 decades there has been intense interest in the clinical significance of the concurrence of manic symptoms in depressed patients. DSM-5 introduced a mixed features specifier for both bipolar depression and major depressive disorder. Studies of the DSM-5 mixed features specifier have generally found a low prevalence of mixed depression. One approach towards increasing the sensitivity of the DSM-5 mixed features criteria is to lower the classification threshold.ObjectivesIn the present study we examine the impact of lowering the DSM-5 diagnostic threshold from 3 to 2 criteria on the prevalence and validity of the DSM-5 mixed features specifier for depression.MethodsFour hundred fifty-nine psychiatric patients in a depressive episode were interviewed by a trained diagnostic rater who administered semi-structured interviews including the DSM-5 Mixed Features Specifier Interview. The patients were rated on clinician rating scales of depression, anxiety and irritability, and measures of psychosocial functioning, suicidality, and family history of bipolar disorder.ResultsIf the DSM-5 diagnostic threshold is lowered from 3 to 2 symptoms, then the prevalence of mixed features based on the DSM-5 majority of episode time frame tripled from 3.9% to 13.1% (n=60). Based on a past week time frame prevalence more than doubled from 9.4% to 22.9% (n=105) going from the 2 and 3 symptom threshold, respectively. There was no difference between the patients with 2 mixed features and patients with 0 or 1 mixed features on family history of bipolar disorder, psychosocial impairment, presence of comorbid disorders, age of onset, history of suicide attempts or psychiatric hospitalization.ConclusionsThe results of the present study do not support lowering the DSM-5-TR diagnostic threshold for the mixed features specifier in depressed patients from 3 to 2.Disclosure of InterestNone Declared

Bipolar disorder, Deafness and Culturality in Psychiatric Home Hospitalization: A Clinical Case

IntroductionMental health in the deaf community is a complex issue. Challenges in diagnosis and treatment arise from a lack of experienced interpreters and difficulties in translating Sign Language to spoken language. Deaf individuals, due to auditory limitations, are more vulnerable to abuse, increasing their risk of mental health disorders, including bipolar affective disorder (BPAD). BPAD is a prevalent, debilitating condition with varied prevalence estimates. Managing it is tough due to its lifelong, unpredictable nature. A new approach called Psychiatric Home Hospitalization Unit aims to provide acute mental health care at home as an alternative to hospitalization.ObjectivesTo show the management of severe bipolar disorder with comorbidity from a Psychiatric Home Hospitalization UnitMethodsA clinical case of bipolar disorder with deafness attended at the Psychiatric Home Hospitalization Unit of our hospital is presented.ResultsA 24-year-old deaf woman borned in Pakistan and later moving to Catalonia, she faced educational challenges but ultimately completed her studies with sign language support. Afterward, she struggled to find suitable employment, and her family had a history of bipolar disorder.She exhibited a sudden change in behavior, characterized by irritability, paranoia, and distrust. Communication was challenging due to her speech difficulties, but assessments using sign language and observation were conducted. Her physical examination was normal, but her speech was disorganized and pressured, suggesting possible auditory hallucinations and thought disturbances. She was hospitalized and diagnosed with bipolar disorder with psychotic features.During her initial hospitalization, she received lithium, olanzapine, clotiapine and benzodiacepines. After discharge, she continued treatment through a home hospitalization service during almost 4 month. During follow-up she presented a course with high affective instability, rapid cycling alternating brief periods of stability with other presenting manic and mixed features with high disorganization.Due to the rapid cycling pattern Valproic acid was considered. Valproic acid was introduced up to 700 mg/d (97.1 mcg/mL). Treatment with lithium carbonate 800 mg/d (0.91 mEq/L) was maintained. Previous antipsychotic regimen was changed to quetiapine 400mg/d, olanzapine 5mg/d. Her condition improved significantly with the adjusted treatment regimen. She was discharged to an outpatient service.ConclusionsDiagnosing and treating bipolar affective disorder (BPAD) in a deaf and mute patient posed unique challenges. The rapid mood cycling pattern and complexity of her case made treatment challenging. Family information and interpreter support were vital. Cultural factors were considered, and home hospitalization was crucial in managing symptoms that lasted over four months.Disclosure of InterestNone Declared

ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation.

Graph convolutional networks (GCNs) have shown success in many graph-based applications as they can combine node features and graph topology to obtain expressive embeddings. While there exist numerous GCN variants, a typical graph convolution layer uses neighborhood aggregation and fully-connected (FC) layers to extract topological and node-wise features, respectively. However, when the receptive field of GCNs becomes larger, the tight coupling between the number of neighborhood aggregation and FC layers can increase the risk of over-fitting. Also, the FC layer between two successive aggregation operations will mix and pollute features in different channels, bringing noise and making node features hard to converge at each channel. In this article, we explore graph convolution without FC layers. We propose scale graph convolution, a new graph convolution using channel-wise scale transformation to extract node features. We provide empirical evidence that our new method has lower over-fitting risk and needs fewer layers to converge. We show from both theoretical and empirical perspectives that models with scale graph convolution have lower computational and memory costs than traditional GCN models. Experimental results on various datasets show that our method can achieve state-of-the-art results, in a cost-effective fashion.

Identifying predictive factors for mood recurrence in early-onset major mood disorders: A 4-year, multicenter, prospective cohort study

We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR. The discrepancies in wake-up times between weekdays and weekends, along with disrupted circadian rhythms, are identified as a notable predictor of ODR. Our findings confirm that we need to be aware of different predictors for each form of mood recurrences in patients with early-onset mood disorders. In clinical practice, we expect that information obtained from the initial assessment of patients with mood disorders, such as mood disorder type, family history of BD, regularity of wake-up time, and disruption of circadian rhythms, can help predict the risk of recurrence for each patient, allowing for early detection and timely intervention.

Study on the separation and mixing laws of wide-grained dense medium in vibration separation fluidized bed

Vibro-fluidized dry coal beneficiation involves the unavoidable mixing of −1 mm coal dust into a fluidized bed, which compromises the stability and homogeneity of the bed density and reduces the effectiveness of fine-grained coal separation. To understand the features of coal powder separation and mixing (-1 mm) in a vibrating fluidized bed, as well as to achieve a uniform and stable bed density, a wide-grained dense medium consisting of −1 mm coal dust and 0.3–0.15 mm magnetite powder was homogeneously mixed in this work. The degree of density separation in the entire bed and the local mixing properties of the wide-grained dense medium were examined. It was discovered that when low vibrational energy was introduced at the same air velocity, particle separation was enhanced compared to the usual fluidized bed. Small bubbles were found to ensnare fine coal dust and migrate upwards, increasing the density separation. The introduction of high vibrational energy led to particle disorder within the bed and an increase in the level of particle mixing. The beneficial effects of the vibration on the bed diminished when the air velocity was gradually increased under the specified vibration conditions. This caused large bubbles to develop more frequently, which increased particle mixing. The 1–0.5 mm fine-grained coal exhibited the best mixing state; it was less affected by variations in air velocity and vibration levels, whereas the −0.5 mm coal dust was more affected by each variable. Furthermore, the particle system achieved a wide range of air velocity regulations without a significant segregation state change under the vibration conditions of f= 25 Hz and A=1 mm and f= 25 Hz and A=2 mm.

Utilization of response surface methodology to optimize the mechanical behaviour of flax/nano TiO2/Epoxy based hybrid composites under liquid nitrogen environment

Linum usitatissimum commonly known as flax fibers, emerges as a promising reinforcement phase for artificial polymer resins, boasting ecological benefits, low density, and easy accessibility. However, the mechanical behavior of such composites hinges crucially on factors such as fiber mat thickness, nanoTiO2 filler content, and the application of cryogenic treatment. Addressing this complex interplay, this study employs a hand lay-up technique for composite construction, subjecting nanocomposite plates to the challenging liquid nitrogen conditions at 77 K post-manufacture. Recognizing the need for an optimized approach, Response Surface Methodology (RSM) based on Box-Benhken designs is employed to enhance the mixing features of linum usitatissimum polymer composites. The study calculates anticipated mechanical strength values through rigorous ANOVA inferential analysis, uncovering the pivotal roles played by fiber mat thickness, nanofiller content, and cryogenic treatment in the two feature interactions (2FI) model components. The methodology proves robust with high R2 values (0.9670 for tensile, 0.9845 for flexural, and 0.9670 for interlaminar shear strength) consistently aligning with experimental findings. The study culminates in identifying optimized parameters for maximal mechanical properties—300 gsm flax fiber thickness, 5 wt.% nano TiO2 concentration, and a 15-min cryogenic treatment—a result that advances our understanding of fundamental factors influencing composite performance and provides practical guidelines for applications in fields requiring superior mechanical strength in challenging environments.

Mixed Features Specifier in Mood Disorder: The Prevalence and Associated Factors

Introduction: Malaysia’s unique cultural landscape may influence the manifestation of mixed features differently than in global studies. We’ve conducted the first study on the prevalence of the Mixed Feature Specifier (MFS) in mood disorders in Malaysia, addresses crucial research gaps to potentially inform culturally sensitive treatment approaches in Malaysia. Methods: This cross-sectional prevalence study was conducted at a psychiatric hospital in Malaysia, using a researcher administered questionnaire, consisted of five parts which includes sociodemographic factors, clinical characteristics, MADRS score, YMRS score and QoL score. Data was analyzed using SPSS version 24.0. Results: 148 patients were recruited into this study. Overall prevalence of MFS among Major Mood Disorder was 29.8%. There were significant associations between ethnicity (X2=9.063; p=0.028), marital status (X2=18.738; p<0.001) and average monthly income (X2=31.534; p=<0.001) with MFS. In terms of clinical characteristics, there were significant associations between number of hospitalizations (X2=41.026; p<0.001), trials of medications (X2=29.540; p<0.001), total number of medications on (X2=42.338; p<0.001), history of ECT (X2=10.590; p=0.001), family history of mood disorder (X2=20.944; p<0.001), suicidal attempts (X2=26.570; p<0.001), history substance use (X2=19.249; p<0.001), compliance to treatment (X2=4.310; p=0.038), YMRS score (X2=16.799; p=0.001) and MADRS score (X2=43.525; p<0.001) with MFS. Conclusion: We identified predictors associated with MFS, such as amount of income, substance use and number of hospitalizations. Sociodemographic and clinical characteristics are significantly associated with MFS, with significant differences between the QoL of MFS and non-MFS patients. This could foster early detection of cases, hence addressing the psychological needs of both MFS and non-MFS patients.

Dermoscopic features of Melasma

Melasma, is a chronic acquired disorder of hypermelanosis of skin which is exposed to ultraviolet radiation. The clinical presentation is most often in the form of hyperpigmented patches over the face in three common patterns: Centrofacial, malar and mandibular. It is commonly seen in pigmented skin phenotypes (Fitzpatrick skin types III-V). The disease has an impact on the quality of life of patients. Dermoscopy is an in vivo noninvasive technique used to examine pigmented and amelanotic skin lesions. The technique is performed using a hand-held self-illuminating device called dermatoscope that visualizes features present under the skin surface. : To study dermoscopic (dermatoscopic) features of melasma and to distinguish between epidermal and dermal melasma based on dermoscopic features.: This study was conducted in an OPD in a clinic on 40 patients of melasma. Dermoscopy was done and their dermatoscopic features were recorded, melasma was classified as epidermal or dermal depending on dermatoscopic features. : Dermatoscopic features of melasma seen were – Accentuation of pseudoreticular pigment network, light to dark brown in colour sparing of the periappendageal region (follicular and sweat gland openings), brown granules, blue- gray perifollicular accentuation honeycomb like reticular pattern and arcuate pigmented lines. 18 patients (45%) showed epidermal type 9 patients (22.5%) revealed dermal type of melasma, 13 patients showed mixed features (epidermal and dermal). Melasma with steroid abuse showed marked erythema and telangiectasia. Dermatoscope is a valuable aid to diagnose, classify and to monitor treatment of melasma.

Improving the recovery performance of superconducting fault current limiters using nitrogen/tetrafluoromethane mixed coolants

The superconducting fault current limiter (SFCL) improves the power grid safety by restricting the drastically increased current when the circuit fault occurs. However, the SFCL suffers from a long recovery time and can hardly recover to the superconducting state before the breaker attempts to reclose, which hinders its practical application. This paper presents the implementation of the rapid-recovery SFCL with efficient heat dissipation even if encountering a large fault current. This is enabled by thermal performance regulation of the working coolant through adding tetrafluoromethane (CF4) into the liquid nitrogen (LN2). The proposed mixed coolant features a high critical heat flux that suppresses the onset of film boiling. Controlled experiments were performed with the fabricated current-limiting coil immersed in the N2/CF4 mixed coolants containing 0, 20 and 40 mol% of CF4. Measured results regarding both quenching and recovery processes are reported, demonstrating an 85.4% reduction in recovery time for the 40 mol% composition compared to the conventionally used pure LN2. Analyses of the electrical parameters of the coil verify its effective quenching performance in the proposed immersion cooling system with an overall current limiting rate of over 55%. These findings lay a foundation for designing the rapid-recovery-type SFCL and facilitating its application in power systems.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

Exploring the causes of COPD misdiagnosis in primary care: A mixed methods study.

Within primary care there exists a cohort of patients misdiagnosed with Chronic Obstructive Pulmonary Disease (COPD). Misdiagnosis can have a detrimental impact on healthcare finances and patient health and so understanding the factors leading to misdiagnosis is crucial in order to reduce misdiagnosis in the future. The objective of this study is to understand and explore the perceived causes of COPD misdiagnosis in primary care. A sequential mixed methods study, quantifying prevalence and features of patients misdiagnosed with COPD in primary care followed by a qualitative analysis to explore perceived causes of misdiagnosis. Quantitative data was collected for 206 patients identified as misdiagnosed with COPD within the INTEGR COPD study (NCT03482700). Qualitative data collected from 21 healthcare professionals involved in providing COPD care and 8 misdiagnosed patients who were recruited using a maximum variation purposive sampling. Misinterpretation of spirometry results was the prevailing factor leading to patients initially being misdiagnosed with COPD, affecting 59% of misdiagnosed patients in this cohort. Of the 99 patients who were investigated for their underlying diagnosis; 41% had normal spirometry and 40% had asthma. Further investigation through qualitative methodology uncovered reluctance to challenge historical misdiagnoses and challenges in differential diagnosis as the underlying explanations for COPD misdiagnosis in this cohort. Patients historically diagnosed with COPD without spirometric evidence are at risk of remaining labelled and treated for COPD despite non-obstructive respiratory physiology, leading to a persistent cohort of patients misdiagnosed with COPD in primary care. The lack of spirometry services during and after the COVID19 pandemic in primary care risks adding to the cohort of misdiagnosed patients. Support from respiratory specialists can potentially help to reduce the prevalence of COPD misdiagnosis in primary care. NCT03482700.

Features of Degenerate Four-Wave Mixing in Liquid Crystal Cell in Dynamic Photorefractivity Mode

Features of Degenerate Four-Wave Mixing in Liquid Crystal Cell in Dynamic Photorefractivity Mode

A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans.

Lung cancer is one of the deadliest cancers globally, and early diagnosis is crucial for patient survival. Pulmonary nodules are the main manifestation of early lung cancer, usually assessed using CT scans. Nowadays, computer-aided diagnostic systems are widely used to assist physicians in disease diagnosis. The accurate segmentation of pulmonary nodules is affected by internal heterogeneity and external data factors. In order to overcome the segmentation challenges of subtle, mixed, adhesion-type, benign, and uncertain categories of nodules, a new mixed manual feature network that enhances sensitivity and accuracy is proposed. This method integrates feature information through a dual-branch network framework and multi-dimensional fusion module. By training and validating with multiple data sources and different data qualities, our method demonstrates leading performance on the LUNA16, Multi-thickness Slice Image dataset, LIDC, and UniToChest, with Dice similarity coefficients reaching 86.89%, 75.72%, 84.12%, and 80.74% respectively, surpassing most current methods for pulmonary nodule segmentation. Our method further improved the accuracy, reliability, and stability of lung nodule segmentation tasks even on challenging CT scans.

Dual contrastive attention-guided deformable convolutional network for single image super-resolution

With its powerful ability to model geometric transformations, the deformable convolutional network brings great improvements for single image super-resolution (SISR). Nevertheless, its location-variant sampling method leads to an escalation in spatial variance as the deformable convolutional layers are stacked, consequently resulting in limited performance. Hence, we propose a novel and effective approach called dual contrastive attention-guided deformable convolutional network (DCADCN) for SISR modeling. Specifically, we propose an attention-guided deformable convolutional module with joint inner and external attention mechanisms to fully exploit the correspondences between input and deformation features and preserve spatial characteristics to the extent possible. Additionally, we propose a dual mixed feature extractor consisting of two parallel sub-paths. This design allows for the learning of diverse and complementary spatial features. Furthermore, contrastive learning is applied to further amplify the role of key features and mitigate the interference of noisy features. Extensive experimental results demonstrate that DCADCN is capable of effectively handling classic SISR, SISR with blind noise, and real-world SISR tasks. Moreover, our method achieves comparable or even better performance with lower computational cost compared to state-of-the-art methods.