Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Improved Outcomes for Patients With Lung Cancer Globally: The Essential Role of Radiology and Nuclear Medicine

PURPOSE Key to achieving better population-based outcomes for patients with lung cancer is the improvement of medical imaging and nuclear medicine infrastructure globally. This paper aims to outline why and spark relevant health systems strengthening. METHODS The paper synthesizes the global lung cancer landscape, imaging referral guidelines (including resource-stratified ones), the reliance of TNM staging upon imaging, relevant multinational health technology assessments, and precisely how treatment selection and in turn patient outcomes hinge upon imaging findings. The final discussion presents data on current global gaps in both diagnostics (including imaging) and therapies and how, informed by such data, improved population-based outcomes are tangible through strategic planning. RESULTS Imaging findings are central to appropriate lung cancer patient management and can variably lead to life-prolonging interventions and/or to life-enhancing palliative measures. Early-stage lung cancer can be treated with curative intent but, unfortunately, most patients with lung cancer still present at advanced stages and many patients lack access to both diagnostics and therapies. Furthermore, half of lung cancer cases occur in low- and middle-income countries. The role of medical imaging and nuclear medicine in lung cancer management, as outlined herein, may help inform strategic planning. CONCLUSION Lung cancer is the number one cancer killer worldwide. The essential role that medical imaging and nuclear medicine play in early diagnosis and disease staging cannot be overstated, pivotal in selecting the many patients for whom measurably improved outcomes are attainable. Prevention synergized with patient-centered, compassionate, high-quality lung cancer management provision mandate that strategic population-based planning, including universal health coverage strategies, should extend well beyond the scope of disease prevention to include both curative and noncurative treatment options for the millions afflicted with lung cancer. Toward Improved Outcomes for Patients with Lung Cancer Globally: The Essential Role of Radiology & Nuclear Medicine.
Delayed Impulsive Control for Lag Synchronization of Delayed Neural Networks Involving Partial Unmeasurable States

In the framework of impulsive control, this article deals with the lag synchronization problem of neural networks involving partially unmeasurable states, where the time delay in impulses is fully addressed. Since the complexity of external environment and uncertainty of networks, which may lead to a result that the information of partial states is unmeasurable, the key problem for lag synchronization control is how to utilize the information of measurable states to design suitable impulsive control. By using linear matrix inequality (LMI) and transition matrix method coupled with dimension expansion technique, some sufficient conditions are derived to guarantee lag synchronization, where the requirement for information of all states is needless. Moreover, our proposed conditions not only allow the existence of unmeasurable states but also reduce the restrictions on the number of measurable states, which shows the generality of our results and wide-application in practice. Finally, two illustrative examples and their numerical simulations are presented to demonstrate the effectiveness of main results.
Targeting EGFR Exon 20 Insertion Mutation in Non–small cell Lung Cancer: Amivantamab and Mobocertinib

Objective To evaluate clinical data regarding the use of amivantamab and mobocertinib for epidermal growth factor receptor (EGFR) exon 20 insertion mutation non–small cell lung cancer (NSCLC) and assess their potential impact on the care of patients. Data Sources A comprehensive literature search of PubMed and Clinicaltrials.gov was conducted using the terms amivantamab, Rybrevant, JNJ-61186372, mobocertinib, Exkivity, TAK-788. Study Selection and Data Extraction Relevant English-language clinical trials were evaluated. Data Synthesis Amivantamab and mobocertinib were Food and Drug Administration (FDA) approved based on phases 1 and 2 studies. Amivantamab demonstrated an overall response rate (ORR) of 40% and median progression-free survival (PFS) of 8.3 months. Patients commonly experienced rash (86%), paronychia (45%), and stomatitis (21%). Mobocertinib demonstrated an ORR of 28% and median PFS of 7.3 months in phase 1/2 study. Patients frequently experienced diarrhea (91%), rash (45%), and paronychia (38%). Cardiac monitoring is recommended with mobocertinib due to risk of QTc prolongation and cardiac failure. Relevance to Patient Care For NSCLC patients who possess an EGFR exon 20 insertion mutation, amivantamab and mobocertinib are indicated as second-line therapy. Ongoing studies are evaluating these therapies as first-line monotherapy and as part of combination regimens in multiple cancer types. Dosage forms, drug interactions, and patient comorbidities should be considered when deciding which of the 2 agents may be most appropriate. Conclusion Amivantamab and mobocertinib target an uncommon NSCLC mutation that has historically marked a poor prognosis because of innate resistance to previously approved EGFR tyrosine kinase inhibitors. Promising results from early phase trials supported accelerated FDA approval.
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks

Meta-learning has become a practical approach towards few-shot image classification, where “a strategy to learn a classifier” is meta-learned on labeled base classes and can be applied to tasks with novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are constructed with data augmentations from unlabeled base classes during meta-training, and we apply embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test. We observe two elements play important roles in UML, i.e., the way to sample tasks and measure similarities between instances. Thus we obtain a strong baseline with two simple modifications — a sufficient sampling strategy constructing multiple tasks per episode efficiently together with a semi-normalized similarity. We then take advantage of the characteristics of tasks from two directions to get further improvements. First, synthesized confusing instances are incorporated to help extract more discriminative embeddings. Second, we utilize an additional task-specific embedding transformation as an auxiliary component during meta-training to promote the generalization ability of the pre-adapted embeddings. Experiments on few-shot learning benchmarks verify that our approaches outperform previous UML methods and achieve comparable or even better performance than its supervised variants.
Relation Matters: Foreground-Aware Graph-Based Relational Reasoning for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods. However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected. In this case, only seeking one-vs-one alignment does not necessarily ensure the precise knowledge transfer. Moreover, conventional alignment-based approaches may be vulnerable to catastrophic overfitting regarding those less transferable regions (e.g., backgrounds) due to the accumulation of inaccurate localization results in the target domain. To remedy these issues, we first formulate DAOD as an open-set domain adaptation problem, in which the foregrounds and backgrounds are seen as the “known classes” and “unknown class” respectively. Accordingly, we propose a new and general framework for DAOD, named Foreground-aware Graph-based Relational Reasoning (FGRR), which incorporates graph structures into the detection pipeline to explicitly model the intra- and inter-domain foreground object relations on both pixel and semantic spaces, thereby endowing the DAOD model with the capability of relational reasoning beyond the popular alignment-based paradigm. FGRR first identifies the foreground pixels and regions by searching reliable correspondence and cross-domain similarity regularization respectively. The inter-domain visual and semantic correlations are hierarchically modeled via bipartite graph structures, and the intra-domain relations are encoded via graph attention mechanisms. Through message-passing, each node aggregates semantic and contextual information from the same and opposite domain to substantially enhance its expressive power. Empirical results demonstrate that the proposed FGRR exceeds the state-of-the-art performance on four DAOD benchmarks.
Tibiofemoral contact force differences between flat flexible and stable supportive walking shoes in people with varus-malaligned medial knee osteoarthritis: A randomized cross-over study

Objective To compare the effect of stable supportive to flat flexible walking shoes on medial tibiofemoral contact force (MTCF) in people with medial knee osteoarthritis and varus malalignment. Design This was a randomized cross-over study. Twenty-eight participants aged ≥50 years with medial knee osteoarthritis and varus malalignment were recruited from the community. Three-dimensional full-body motion, ground reaction forces and surface electromyograms from twelve lower-limb muscles were acquired during six speed-matched walking trials for flat flexible and stable supportive shoes, tested in random order. An electromyogram-informed neuromusculoskeletal model with subject-specific geometry estimated bodyweight (BW) normalized MTCF. Waveforms were analyzed using statistical parametric mapping with a repeated measures analysis of variance model. Peak MTCF, MTCF impulse and MTCF loading rates (discrete outcomes) were evaluated using a repeated measures multivariate analysis of variance model. Results Statistical parametric mapping showed lower MTCF in stable supportive compared to flat flexible shoes during 5–18% of stance phase (p = 0.001). For the discrete outcomes, peak MTCF and MTCF impulse were not different between the shoe styles. However, mean differences [95%CI] in loading impulse (-0.02 BW·s [-0.02, 0.01], p<0.001), mean loading rate (-1.42 BW·s-1 [-2.39, -0.45], p = 0.01) and max loading rate (-3.26 BW·s-1 [-5.94, -0.59], p = 0.02) indicated lower measure of loading in stable supportive shoes compared to flexible shoes. Conclusions Stable supportive shoes reduced MTCF during loading stance and reduced loading impulse/rates compared to flat flexible shoes and therefore may be more suitable in people with medial knee osteoarthritis and varus malalignment. Trial registration Australian and New Zealand Clinical Trials Registry (12619000622101).
Cohort difference in job environments: the mediating effect of organizational identification on the relationships between mindful leadership and self-spirituality

Abstract This paper aims to examine the effect of mindful leadership on employees’ self-spirituality, and the mediating effect of organizational identification, and the moderating effect of cohort differences in machinery companies. This study collected data from three different periods. The result demonstrated that organizational identification mediated between mindful leadership and employees’ self-spirituality, which boosted to deliver the peculiarity and attractiveness of their hardworking regarding personal capability. Besides, larger cohort differences negatively moderated between mindful leadership and employees’ self-spirituality, and vice versa. The main dedication is the application of critical surveys depending on important supportive elements in the associated territory.
Imaging Dopaminergic Neurotransmission in Neurodegenerative Disorders

Imaging of dopaminergic transmission in neurodegenerative disorders such as Parkinson disease (PD) or dementia with Lewy bodies plays a major role in clinical practice and in clinical research. We here review the role of imaging of the nigrostriatal pathway, as well as of striatal receptors and dopamine release, in common neurodegenerative disorders in clinical practice and research. Imaging of the nigrostriatal pathway has a high diagnostic accuracy to detect nigrostriatal degeneration in disorders characterized by nigrostriatal degeneration, such as PD and dementia with Lewy bodies, and disorders of more clinical importance, namely in patients with clinically uncertain parkinsonism. Imaging of striatal dopamine D2/3 receptors is not recommended for the differential diagnosis of parkinsonian disorders in clinical practice anymore. Regarding research, recently the European Medicines Agency has qualified dopamine transporter imaging as an enrichment biomarker for clinical trials in early PD, which underlines the high diagnostic accuracy of this imaging tool and will be implemented in future trials. Also, imaging of the presynaptic dopaminergic system plays a major role in, for example, examining the extent of nigrostriatal degeneration in preclinical and premotor phases of neurodegenerative disorders and to examine subtypes of PD. Also, imaging of postsynaptic dopamine D2/3 receptors plays a role in studying, for example, the neuronal substrate of impulse control disorders in PD, as well as in measuring endogenous dopamine release to examine, for example, motor complications in the treatment of PD. Finally, novel MRI sequences as neuromelanin-sensitive MRI are promising new tools to study nigrostriatal degeneration in vivo.
Phoneme acquisition in French-speaking children with moderate hearing loss

ABSTRACT The current study aims to provide a more precise understanding of phoneme acquisition by children with moderate hearing loss (MHL), since they have been an under-studied population among children with hearing loss. The acquisition of sounds by 15 children with MHL (mean: 3;11 year-old) was compared to that by six younger normal-hearing (YNH) children (mean: 2;8 year-old). All the children were video- and audio-recorded in interaction with one or both parents in two semi-spontaneous tasks, and during a naming task. All their productions were analysed in terms of percentage of accuracy and substitution patterns. Similarities and differences between the two populations were observed. Globally, the phoneme level of accuracy of children with MHL was similar to that of YNH, but with a general delay. The observation of substitution patterns revealed differences between the two populations, such as prevalent fortition of fricatives and /ʁ/ by children with MHL, which was unusual among YNH children. Another difference was the prevalent posteriorization of coronal fricatives by children with MHL, whereas anteriorization of post-alveolar fricatives was more common in YNH children. These findings highlight the role of perception and language experience in phonological acquisition, showing that an atypical hearing experience may not only cause a delay, but also the emergence of a specific developmental path.
“They’re all struggling as well”: social and economic barriers and facilitators to self-managing chronic illness among marginalized people who use drugs

ABSTRACT Purpose Self-management is recommended for addressing chronic conditions, and self-management programmes improve health behaviours and outcomes. However, social and economic factors have been neglected in self-management research, despite their relevance for marginalized groups. Thus, we aimed to explore barriers and facilitators that influence self-management among socioeconomically marginalized people who use drugs (PWUD). Methods Using community-based participatory methods, we developed a qualitative interview guide and conducted peer-led recruitment. Participants were admitted into the study after self-identifying as using non-prescribed drugs, having a chronic health issue, and experiencing socioeconomic marginalization. Data were analysed using reflexive thematic analysis, taking a relational autonomy lens. Results Participants highlighted substantial barriers to managing their health issues, mostly stemming from their social and economic environments, such as unstable housing, low income, lack of supportive social networks, and negative healthcare experiences. Participants also described how their ability to self-manage their chronic conditions benefited from specific aspects of social interactions, including close relationships, community connectedness, and engaging in peer support. Conclusions Our findings suggest that structural interventions are needed to support self-management among marginalized PWUD, especially stable housing. Self-management supports for PWUD would benefit from including a range of low-barrier community-based options, peer work opportunities, and advocacy for needs.
Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.

Background The heterogeneous nature of COPD complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods We included 4,496 smokers with available data from their enrollment and 5-year follow-up visits in the Genetic Epidemiology of COPD (COPDGene) study. We constructed linear regression (LR) and supervised random forest (RF) models to predict 5-year progression in FEV1 from 46 baseline features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results Predicting the change in FEV1 over time is more challenging than simply predicting the future absolute FEV1 level. For RF, R-squared was 0.15 and the area under the ROC curves for the prediction of subjects in the top quartile of observed progression was 0.71 (testing) and respectively, 0.10 and 0.70 (validation). RF provided slightly better performance than LR. The accuracy was best for GOLD1-2 subjects and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD. Conclusion RF along with deep phenotyping predicts FEV1 progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.
Promising effect of multi-family therapy on BMI, eating disorders and perceived family functioning in adolescent anorexia nervosa: an uncontrolled longitudinal study

ABSTRACT This contribution reports on the results of an uncontrolled longitudinal study investigating the effects of a Multi-Family Therapy (MFT) as treatment for adolescent Anorexia Nervosa (AN) and the role played by perceived family functioning in these effects. 150 patients (144 females) and their families took part in an MFT and were assessed at the beginning, middle, end of the therapy, as well as 6 and 12 months after the end of therapy. BMI, eating disorders symptomatology (EDI-II) and perceived family functioning (FAD) all improved over the time of MFT, and these benefits remained at both times of follow-up. Ultimately, we found that the improvement of some dimensions of family functioning (i.e., roles, communication, and general family functioning) mediated the improvement of several dimensions of symptomatology (i.e., ineffectiveness, impulsivity, social insecurity, interpersonal distrust). These observations provide further support for MFT as useful for improving mental health, weight restoration, and perceived family functioning of adolescents with AN.
Porcine model of neurocysticercosis by intracarotid injection of Taenia solium oncospheres: Dose assessment, infection outcomes and serological responses

Background Neurocysticercosis (NCC) is the infection of the human central nervous system (CNS) by Taenia solium larvae that cause significant neurological morbidity. Studies on NCC pathophysiology, host-parasite interactions or therapeutic agents are limited by the lack of suitable animal models. We have previously reported that carotid injection of activated T. solium oncospheres directs parasites into the CNS and consistently reproduces NCC. This study assessed the minimal dose required to consistently obtain NCC by intracarotid oncosphere injection and compared antigen and antibody response profiles by dose-group. Methods/Principal findings Three groups of pigs were infected with either 2500 (n = 10), 5000 (n = 11), or 10000 (n = 10) oncospheres. Two pigs died during the study. Necropsy exam at day 150 post-infection (PI) demonstrated viable NCC in 21/29 pigs (72.4%), with higher NCC rates with increasing oncosphere doses (4/9 [44.4%], 9/11 [81.8%] and 8/9 [88.9%] for 2500, 5000, and 10000 oncospheres respectively, P for trend = 0.035). CNS cyst burden was also higher in pigs with increasing doses (P for trend = 0.008). Viable and degenerated muscle cysticerci were also found in all pigs, with degenerated cysticerci more frequent in the 2500 oncosphere dose-group. All pigs were positive for circulating parasite antigens on ELISA (Ag-ELISA) from day 14 PI; circulating antigens markedly increased at day 30 PI and remained high with plateau levels in pigs infected with either 5000 or 10000 oncospheres, but not in pigs infected with 2500 oncospheres. Specific antibodies appeared at day 30 PI and were not different between dose-groups. Conclusion/Significance Intracarotid injection of 5000 or more oncospheres produces high NCC rates in pigs with CNS cyst burdens like those usually found in human NCC, making this model appropriate for studies on the pathogenesis of NCC and the effects of antiparasitic treatment.
Integrative understanding of immune-metabolic interaction

Recent studies have revealed that the immune system plays a critical role in various physiological processes beyond its classical pathogen control activity. Even under a sterile condition, various cells and tissues can utilize the immune system to meet a specific demand for proper physiological functions. Particularly, a strong link between immunity and metabolism has been identified. Studies have identified the reciprocal regulation between these two systems. For example, immune signals can regulate metabolism, and metabolism (cellular or systemic) can regulate immunity. In this review, we will summarize recent findings on this reciprocal regulation between immunity and metabolism, and discuss potential biological rules behind this interaction with integrative perspectives.