Positive adjustment to chronic diseases reduces psychiatric comorbidity and enhances quality of life. Very little is known about the benefit of internet-based and mobile-based Cognitive Behavioral Therapy (IM-CBT) on physical outcomes and its reciprocal interactions with psychiatric outcomes, the active therapeutic elements, and effect moderators among people with major chronic medical conditions. In this systematic review and meta-analysis (PROSPERO: CRD42022265738), CINAHL of Systematic Reviews, MEDLINE, PsycINFO, PubMed, Web of Science are systematically searched up to 1 June 2022, for randomized controlled trials (RCTs) comparing IM-CBT against non-CBT control condition(s) among people with chronic disease(s). Primary outcomes include improvements in psychiatric symptoms  from baseline to post-intervention and follow-ups. Secondary outcomes include improvements in physical distress . Among 44 RCTs (5077 patients with seven different chronic diseases), IM-CBT improves depressive symptoms, anxiety symptoms, and general psychological distress at post-intervention and across follow-ups, and improves physical distress and functional impairment at post-intervention. Preliminary evidence suggests that behavioral modification and problem-solving could be necessary components to reduce psychiatric symptoms in IM-CBT, whereas cognitive restructuring, psychoeducation, and mindfulness elements relate to reduced physical distress. IM-CBT shows stronger benefits in chronic pain, cancer, arthritis, and cardiovascular disease, relative to other conditions. Changes in psychiatric symptoms and physical distress prospectively predict each other over time. IM-CBT is an effective intervention for comprehensive symptom management among people with chronic diseases. Growing numbers of people live with chronic ill health and compromised quality of life over the past decades1, among which one-third experience multiple conditions2. Interventions for mental health are also prioritized to be integrated into the management of chronic medical conditions6. Those patients are 2-3 times more likely to have comorbid mental ill health such as depressive/anxiety disorders relative to the general population7. Comorbid physical and psychiatric conditions could jointly predict poorer prognosis5 and add financial and psychosocial burden8. With the ever-increasing burden on the healthcare system, digitalizing the management of chronic conditions11 could overcome practical barriers such as immune compromise, mobility difficulties, shortage of clinical personnel, and health disparity13.Chronic diseases are responsible for not only deaths but also years lived with disability, a common expansion of morbidity15 and increase the accessibility of care for underserved patients17. Two meta-analyses of different chronic diseases19 and one systematic review of people with rheumatic conditions20 have documented the effectiveness of internet-based CBT in reducing psychiatric and/or physical symptoms. However, previous work did not comprehensively study how IM-CBT effects might differ across various diagnostic conditions and/or health outcomes. More importantly, very little is known about the therapeutic elements specifically responsible for the improved clinical outcomes, the effect moderators, and the reciprocity between mental health outcomes and secondary physical health outcomes.The clinical benefits of specialized psychological treatment namely Cognitive Behavioral Therapy (CBT) delivered across the internet and/or mobile devices [Internet-based and mobile-based CBT (IM-CBT)] for people with chronic diseases should be rigorously reviewed. IM-CBT has been shown to be as effective as face-to-face CBTThis systematic review and meta-analysis aims to examine the effectiveness of IM-CBT in reducing psychiatric symptoms among patients across most common chronic medical conditions in randomized controlled trials. In-depth analyses were also conducted on active CBT treatment components, the influence of patient-related/treatment-related factors, and the prospective association between resulting psychiatric symptoms and physical distress.IM-CBT relates to reduced psychiatric symptoms and physical distress, with the two improvements prospectively predicting each other. Among the CBT components, behavioral modification and problem-solving reduce psychiatric symptoms whereas cognitive restructuring, psychoeducation, and mindfulness reduce physical distress. IM-CBT benefits patients with chronic pain, cancer, arthritis, and cardiovascular disease more, psychologically and physically, relative to those with other diseases. Our results attest the clinical utility of IM-CBT for patients with chronic diseases.64 reporting 48 IM-CBT-to-control comparisons among a total of 5077 patients . Descriptive information on included studies is summarized in Table The study selection process is shown in Fig. 64, 20 in Europe 62, 10 in Australia50, and 2 in Asia 57. Three (6.82%), 24 (54.55%), and 17 (38.64%) studies were assessed to have low, some, and high risks of overall bias, respectively SD = 13.27) years  , cancer , arthritis , cardiovascular disease , diabetes , HIV , multiple sclerosis , and different chronic diseases . Comorbid physical or psychiatric conditions were reported in 23 (52.27%) studies. Complementary treatments for either physical or comorbid psychiatric conditions were reported in 22 (50.00%) studies. For details, see Supplementary Table Included patients had a mean age of 47.61 , alongside videoconferences  and mobile apps ; they were generally short in duration (<12 sessions)19  and guided12 . Control groups included active  or non-active  types. The active control condition included information/education (k = 10 comparisons), discussion forum (k = 5), relaxation (k = 2), attention control (scheduled contact) (k = 2), supportive therapy (k = 1), computerized cognitive remediation therapy (k = 1), and lifestyle management (k = 1). For details, see Supplementary Tables Interventions across studies were predominantly delivered through web-based modules     Figs. 23,29,37,40 and combined depressive and anxiety symptoms40 were significant at follow-up(s) only: at first follow-up , and last-follow-up .The effects on decreased PTSD symptomsg = 0.184)   Fig.  and func84) Fig.  were smaResults are summarized in Table B = 0.761, 95% CI , p < 0.00162. Likewise, decreased physical distress at post-intervention prospectively predicted decreased psychiatric symptoms at follow-ups, B = 1.456, 95% CI , p = 0.00162. The results showed bidirectional positive associations 51, the majority of studies (ks = 46 comparisons) included both elements. IM-CBT was effective for reducing psychiatric symptoms when the protocols included behavioral modification  or problem-solving . IM-CBT protocols were equally effective for reducing psychiatric symptoms with or without cognitive restructuring, psychoeducation, or mindfulness . Meanwhile, IM-CBT significantly reduced physical distress when the protocols included cognitive restructuring 64, psychoeducation 63, or mindfulness 60, but the effects were non-significant without these elements. The effects on reducing psychiatric symptoms or physical distress were independent of the total number of therapeutic elements (ps ≥ 0.407). Results are summarized in Table Therapeutic elements within individual studies are summarized in Supplementary Table g = 0.391, p < 0.001)63, cancer 48, arthritis 60, cardiovascular disease 61, and different chronic diseases 64, but not those with diabetes, multiple sclerosis, or HIV (ps ≥ 0.154). Decreased symptoms were reported by patients who were not on concurrent psychotherapies for psychiatric condition(s) 64 but not those receiving psychotherapies (p = 0.136). IM-CBT effects were observed independent of patients’ gender and presence of comorbidity (ps ≥ 0.287). Age as a potential moderator was not analyzed because most studies were conducted among people with a wide range of age 62 while only mean age (SD) was available for all included studies.IM-CBT related to decreased psychiatric symptoms among patients diagnosed with chronic pain 62 relative to ≥12 sessions  but independent of guidance (p = 0.814). Effects were significant only among interventions delivered through web-based modules , relative to interventions delivered through videoconference57 and mobile app42. Results are summarized in Table The effect sizes for the IM-CBT effects on decreased psychiatric symptoms were larger with fewer than 12 sessions  than active  control groups, p = 0.047. Meanwhile, the effects on physical distress were significant only when studies showed some-to-high risks of overall bias, low-to-moderate (≤20%) attrition rate, utilization of intention-to-treat analysis, and/or non-active control groups  on psychiatric symptoms, physical distress, and the reciprocity between improved mental and physical health outcomes among people with chronic diseases. We specifically investigated the therapeutic elements and effect moderators. Our conclusions were based on 44 RCTs (48 comparisons) conducted across 11 countries, with a total of 5077 patients affected by seven different chronic diseases. We found immediate and/or sustained effects of IM-CBT on reducing psychiatric symptoms and physical distress, with improved psychiatric symptoms and physical distress prospectively positively predicting each other. Behavioral modification and problem-solving benefited psychiatric symptoms, whereas cognitive restructuring, psychoeducation, and mindfulness benefited physical distress. Diagnoses of chronic diseases and lower intervention intensity moderated the clinical benefits of IM-CBT, suggesting the need to investigate its impact in more diverse chronic diseases and the cautions in applying different therapeutic elements among different patients.67 have become more common in recent years69. With comparable effectiveness as face-to-face CBT15, IM-CBT could contribute added values over its conventional counterparts66.This meta-analysis went beyond previous studies by robustly validating the clinical benefits of IM-CBT with more types of chronic diseases, more varied psychiatric and physical outcomes, and longer durations of follow-up. Telemedicine and digital psychotherapeutic interventions70, rheumatoid arthritis71, COPD72, cardiovascular disease73, and kidney disease74. Adding to previous evidence on the IM-CBT effects on decreased depressive and anxiety symptoms, this study reported some of the first evidence that suggests sustainability of the positive effect over time and across other psychiatric conditions including PTSD symptoms and general psychological distress. The effects remained significant controlling for study quality. It is important to note, however, that included studies focused on depression, anxiety, PTSD, and general psychological distress only although we intended to review studies with all kinds of psychiatric conditions. Beyond the conventional IM-CBT approaches examined within the current review, a previous meta-analysis on technology-based acceptance and commitment therapy (20 articles/interventions)75 reported intervention effects for functioning and acceptance-related outcomes, whereas another systematic review on internet-based mindfulness-based interventions (11 articles on 10 interventions)76 reported intervention effects for psychiatric symptoms, coping, and/or quality of life. It warrants further investigation, however, whether the clinical benefits of conventional IM-CBT and its extensions are uniformly comparable or domain-specific77. Taken together, current and previous evidence generally supported the potential of IM-CBT and its third-wave extensions among patients with chronic diseases.This meta-analysis confirmed the benefits of IM-CBT on the mental health of people with chronic diseases. The effectiveness of CBT for alleviating depressive and/or anxiety symptoms has been attested among people with chronic pain79 and null77 effects have been reported among limited scopes of chronic diseases. Similarly, mixed findings have been identified on the physical health benefits of IM-CBT20. Although our moderator analyses suggested that IM-CBT could ameliorate physical symptoms and functional impairment, the significant effects could be attributable to methodological factors such as comparisons with non-active control groups63 and some-to-high risk of bias64. Non-significant effects could be attributable to high attrition53. Additionally, in order to obtain a complete picture on IM-CBT effects on physical distress, our current analysis maximally included all available data categorized under the four pre-specified subcategories, despite potential heterogeneity across specific outcomes. Further meta-analytic reviews are therefore warranted to look into IM-CBT effects on specific individual outcomes under physical distress.This study assessed comprehensive dimensions of physical distress, namely physical symptoms, functional impairment, self-rated ill health, and objective physiological dysfunction. Existing evidence on physical health benefits of CBT in chronic diseases has been relatively mixed – both positive80. The reciprocity suggested that the two dimensions of health are complementary to and benefit each other in the long run. Common psychiatric and physical conditions share etiology and maintenance factors5. Symptom and treatment management plans could consider psychiatric and physical conditions as a larger syndrome towards a holistic symptom management for people with chronic diseases81.Our findings showed that IM-CBT-related decrease in psychiatric symptoms and physical distress positively predicted each other in the long run, adding to existing cross-sectional evidence on the positive associations in previous empirical studies or meta-analyses82, most if not all previous studies overlooked the heterogeneity in therapeutic elements across interventions that share the same label of CBT20. This could limit a full understanding on the therapeutic mechanism(s) of IM-CBT  responsible for clinical benefits on physical and mental health83.While CBT practically involves skill sets that could be theoretically classified into different categories84. Problem-solving, denoting systematic procedures to identify and address everyday life problems and enhance coping skills, has been found to decrease depressive symptoms among older adults with physical conditions85 and among psychiatric patients in the primary care setting86.We observed that two therapeutic elements, namely behavioral modification and problem-solving, were most commonly adopted across included RCTs . As such, cautions are warranted in interpreting these two factors as potential moderators of IM-CBT effects on psychiatric symptoms. While our analyses could not fully confirm on an empirical level that the two components are necessary conditions to ensure the benefits on physical and mental health of people with chronic diseases, theoretically, the clinical implications of behavioral modification and problem-solving have been documented in existing literature. For example, subordinate strategies within behavioral modification such as behavioral contracting and physical exercise could enhance activity level and healthy lifestyle, which in turn serve as protective factors of mental health87. Relatedly, psychoeducation equips people with knowledge on chronic diseases and guides them to be aware of disease-related cognition and behaviors88. Both could increase health literacy and relieve psychological burden, leaving these people with more motivation and energy necessary for symptom management such as medication adherence and health-promoting behaviors3. In addition, mindfulness, as the ability or practice to observe one’s present sensations, thoughts, and feelings with an open and nonjudgmental attitude89, has been found to improve pain and fatigue, blood pressure, and weight control among people with different chronic diseases, although uncertainties exist in its mechanism, variability, and consistency across different modalities90.Cognitive restructuring, psychoeducation, and mindfulness were shown to be important therapeutic components for reducing physical distress. Cognitive restructuring replaces negative and inaccurate thoughts with more realistic and adaptive ones49.The effects of IM-CBT in reducing psychiatric symptoms were more established among chronic pain, cancer, arthritis, and cardiovascular disease, but not diabetes, multiple sclerosis, and HIV. However, it should be noted that the latter three conditions have been investigated by fewer studiesThe significant IM-CBT effects among interventions delivered via web-based modules but not videoconferences and mobile apps could be due to the fact that it was the predominant format adopted across eligible studies. However, because there were few studies on interventions delivered via videoconferences and mobile apps, which also tended to be less methodologically reliable  compared to those delivered via web-based modules, we were not able to fully assess the impact of delivery platform on IM-CBT effects. More systematic investigation is needed on whether there is true advantage of delivering IM-CBT over particular types of platforms.91, a part of the IM-CBT effects could be protocol-nonspecific, and thus frequent engagement in the active control activities could be inversely related to psychiatric symptoms or physical distress over a period of time. In addition, our findings could call for more attention and empirical investigation to reconsider whether the effects of IM-CBT vary, positively, as functions of treatment duration and/or frequency. The association between intervention duration (number of sessions) and outcome could be curvilinear instead of linear, meaning a possible diminishing marginal benefit after an optimal number of sessions92. Our findings were indeed consistent with previous evidence suggesting lower dose as a cost-effective design94. Short intervention with frequent breaks has been suggested to be useful for accommodating fatigue in CBT for adolescents with chronic diseases88. Lower intervention intensity has also been recommended for people with poorer general health95, such as those with chronic diseases in the current meta-analysis. Frequent reminders on the intervention could inadvertently result in notification fatigue and increase non-adherence that has been observed in digital interventions among patients with chronic diseases65. In the current study, we observed that non-adherence (different from attrition) information was insufficiently reported and thus we could not include this variable in the formal analyses. Taken together, these observations invite an open discussion on optimizing the prescription of IM-CBT in order to maximize its clinical benefits for patients with chronic diseases.Surprisingly, the effects of IM-CBT on reducing psychiatric symptoms and physical distress were stronger with fewer sessions (<12 sessions) and thus shorter intervention durations. The effects for physical outcomes were similarly contingent upon fewer intervention sessions and lower intervention frequency. We followed these up with chi-squared tests, and noticed that on a methodological level, interventions with longer duration (≥12 sessions) and higher frequency tended to include no guidance . Interventions with higher frequency were also more likely to include active control groups , and interventions with more sessions were more likely to target patients with physical and/or psychiatric comorbidity. These variations across the RCTs in this review in terms of design and quality suggest the importance of considering the multidimensional sources of therapeutic benefits. We found that active control group was a significant moderator. Based on the common factors theoryThis quantitative synthesis considered a wide range of chronic diseases and examined a large number of psychiatric and physical outcomes within IM-CBT for people with chronic diseases, as well as the positive prospective associations between physical and mental health outcomes. Effective individual therapeutic elements for reducing psychiatric symptoms and physical distress were identified, and patient-related/treatment-related moderators affecting the clinical responses were examined. Our evidence points to a clear direction for developing a holistic support care service for these people.This meta-analysis has some limitations. We pooled the results despite the technical and clinical variations that exist across the included studies. Effect sizes for physical distress were synthesized under four subcategories  although there could be disparity in specific outcomes under each subcategory. Our moderator analyses were conducted on composite constructs of psychiatric symptoms and physical distress instead of the specific constructs. These procedures were applied in order to maximize the number of comparisons. Still, the pooled effect sizes and the moderator analyses might be restricted by the existing number of studies on certain outcomes or features. Some outcomes at follow-up timepoints were missing for synthesis, and differences across subgroups could be left undetected due to a lack of statistical power in the analysis of small samples. Finally, the existing evidence base is biased towards high-income countries/regions, restricting generalizability of the findings to less developed parts of the world.In conclusion, internet-based and mobile-based cognitive behavioral therapy (IM-CBT) could be implemented in clinical settings in order to produce meaningful benefits on reducing psychiatric symptoms and physical distress among patients with chronic diseases. It is likely that the positive effects of IM-CBT on physical and mental health reciprocally benefit each other in the long run. IM-CBT could be particularly beneficial for people within some chronic diseases, while specific therapeutic elements could be key drivers of clinical benefits. It is important for medical scientists and clinicians to consider the fundamental driving forces of positive therapeutic changes in patients, as quality matters more than quantity in IM-CBT. The present findings could also be applicable to psychological services amid large-scale disasters, such as the COVID-19 pandemic, natural hazards, and wars, when physical comorbidities are more likely, restrictions are put on mobility, or the physical environment is not conducive to face-to-face interventions.96, and was pre-registered on PROSPERO (CRD42022265738). Any deviations were outlined and explained in Supplementary Note chronic diseases, cognitive behavioral therapy, psychiatric symptoms, study design. The detailed search algorithm is documented in Supplementary Note This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic review and Meta-analysis (PRISMA) guidelinesPERO CRD40222657381. This study was set out to focus on the more conventional types of CBT, which focus more on modifying and controlling behaviors, thoughts and emotions, relative to the third-wave extensions, which alternatively focus more on acceptance and mindfulness approaches97. In practice, their boundaries could be less clear-cut, and therefore in cases where interventions included a mix of cognitive-behavioral and third-wave elements, our key criterion to decide whether the interventions were eligible was whether they were predominantly defined by the cognitive and/or behavioral elements as opposed to the third-wave elements. Studies were also excluded if the treatment group contained any in-person psychosocial interventions.E.T.F.Y., T.K.L., and P.B.S. selected the articles and extracted data; disagreements were resolved through discussion with T.J.T., L.K.Y.M., and W.K.H. Only English articles published in peer-reviewed journals were considered. The current study reviewed randomized clinical trials that compared psychiatric symptoms between IM-CBT and non-CBT control condition(s) among patients diagnosed with chronic diseases listed on ICD-11 for ≥3 months. Because it is not quite possible to include the great variety of chronic diseases in one single systematic review/meta-analysis, we generated a list of common chronic diseases by referring to leading causes of disability-adjusted life years in the Global Burden of Disease Study 2015 in The Lancet98, and were categorized into low risk, some concerns, or high risk 99. Secondary outcomes included improvements in physical distress  from baseline to different timepoints. If studies included multiple treatment/control arms, each eligible comparison was separately considered, with the sample size of the treatment/control arm divided correspondingly to avoid double counting100.Primary outcomes included improvements in psychiatric symptoms  from baseline to (1) post-intervention, (2) first follow-up, and (3) last follow-up. When a study included multiple instruments for the same psychiatric outcome, only one scale was chosen based on hypothesized frequency of useg  with 95% CI for each outcome based on the change score from baseline to post-intervention (or to follow-ups) between the intervention and control groups101. Correlations between scores within the same group was set at 0.7102. If insufficient baseline data was reported , Hedge’s g was calculated based on cross-sectional comparison(s) between the intervention and control groups. Group means and standard deviations, if not readily available for quantitative syntheses, were converted from other statistics  indices were calculated to indicate the presence and the degree of heterogeneity across results. Analyses with a random-effects approach were performed using Comprehensive Meta-Analysis version 3.0.To statistically account for any baseline differences, we calculated the Hedge’s The prospective associations between changes in psychiatric symptoms and changes in physical distress were examined in two meta-regressions, one regressing the effect size of physical distress at follow-ups on that of psychiatric symptoms at post-intervention and one regressing the effect size of psychiatric symptoms at follow-ups on that of physical distress at post-intervention.Q-tests and meta-regressions: psychiatric symptoms/physical distress, demographics, medical profile, complementary treatments, intervention delivery platform, presence of guidance 12, intervention duration/frequency, therapeutic elements (i.e., behavioral modification, cognitive restructuring, problem-solving, psychoeducation, mindfulness; Supplementary Note 82, assessment schedule, control type, overall risk of bias, attrition rate, and the use of intention-to-treat analysis. To address dependency issues, multiple effect sizes from the same source were averaged in all meta-analytic procedures103.In the subgroup analyses, demographic and medical characteristics of the patients and the characteristics and methodology of the included interventions were investigated with 104. In cases of significant asymmetry, results were statistically adjusted with the trim-and-fill method105.Risk of publication bias was assessed using funnel plots and the Egger test of asymmetrySupplementary Material


Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training  and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases. Gait is a characteristic pattern of movements employed to move from one place to another . Human bRe-identification (re-ID) is the process of matching biometrics over time to re-establish identity. The re-ID task has previously been attempted using a variety of sensing modalities  that generally acquire data on either body motion or applied forces during gait . In the Force platforms are a natural complement to video cameras because they circumvent the aforementioned limitations and are a rich source of kinetic data . These devices measure ground reaction forces (GRFs) along three orthogonal axes (and GRMs . The dirand GRMs ).matching approach, any number of IDs, not seen during training, can be re-identified at test time, provided that there is at least one prior sample. Thus, the implementation of DML would increase the scalability of force platform-based re-ID systems.Despite the apparent prospect of force platforms as sensors for the re-ID task, there have only been a handful of studies in the field ,18,19,20We utilized a large force platform dataset that was purpose built for re-ID of persons and, thus, had the most complex set of walking conditions. The dataset contained 5587 walking trials from 193 IDs, with both intra- and inter-individual variations in clothing, footwear, and walking speed, as well as inter-individual variations in time between trials. A public version of the dataset, named ForceID A, contains data from 184 IDs, who consented to their data being published online (see data availability statement).To provide scope for future DML model design, we evaluated several different baseline DNN architectures at zero-shot re-ID. Two-layer fully connected neural networks (FCNNs) slightly outperformed more complex architectures over seven-fold cross validation, achieving 85% accuracy in our challenging evaluation setting, where there was only one prior sample per ID available to compare with each query sample.https://github.com/kayneduncanson1/ForceID-Study-1.We analyzed the combined effects of changes in walking speed and footwear between measurement instances on re-ID performance. Accuracy across all models on same-speed, same-footwear comparisons (the easiest) was 28% higher than accuracy on cross-speed, cross-footwear comparisons (the hardest). The code repository for this study can be found at Previous studies on force platform-based re-ID also provide an incomplete picture on the effects of task and environmental constraints that are known to alter gait . Models The state of vision-based systems for gait-based person re-ID has been reported extensively in the literature . The staGait REcognition in the Wild (GREW) dataset) of 26,345 IDs walking in diverse public environments with variations in camera angle, lighting, background, walking speed, clothing, object carriage, footwear, and walking direction, as well as the presence of occlusions to the camera field of view. Six models (three DML models and three classification models) that were state-of-the-art at the time on standard benchmark datasets were evaluated at zero-shot re-ID on a test set of 6000 IDs from this dataset. Four out of six of the models almost completely failed . ThPoseGait —44% and PoseGait —46%), yeAll studies on force platform-based re-ID have been done in the many-shot setting using classification models. Connor et al.  providedN prior samples i,T has been shown to influence the loss and, thus, the optimization of P of candidate M of candidate Given a query gait sample ion of θ . The ‘baA is the total number of triplets in The training loss outliers .The dataset used in this study is one of the largest force platform datasets and it has the most complex set of walking conditions, because it was purpose built for evaluating person re-ID systems. It contains 5587 walking trials  from 193 IDs  slower than preferred speed samples, 1900 (≈34%) preferred speed samples, and 1795 (≈32%) faster than preferred speed samples. Even though speed was not measured directly, there was assumed to be inter-individual variation in self-selected speeds within each category [category .i corresponds to whether samples from preferred speed only (PS) or all speeds (AS) were included, while j corresponds to whether samples from all IDs (AF), or just those who wore the same footwear between sessions (SF), were included. The subsets were:In these experiments, four subsets of the dataset were utilized, each containing a different combination of walking speed and footwear conditions. Each subset was labeled with notation Each of the data subsets were distributed into training, validation, and test sets. To perform zero-shot re-ID, these sets each contained a distinct group of IDs. To cycle all IDs through validation and test sets, performance was evaluated over seven-fold cross-validation. The distribution of IDs in training, validation, and test sets was approximately A 50 e values .All signals were low-pass filtered  were imFCNNCNNCLSTMNNConvolutional bi-directional long short-term memory neural network (C-Bi-LSTMNN)Convolutional transformer neural network (CTNN).This was the first time a C-Bi-LSTMNN or CTNN had been implemented for force platform-based re-ID. FCNNs, CNNs, and CLSTMNNs had been implemented previously, though comparison between these architectures is difficult due to differences in re-ID system design between studies . This architecture contained two fully connected layers separated by batch normalization (BN) and exponential linear unit (ELU) activation, respectively (referred to herein as a fully connected module) [L remained the same. Initially, C was set to 32 for the first convolution and was then increased by a factor of two for each convolution thereafter  ,52. To gtivation ) in betwall candidate priors. A more challenging method was implemented in this study, whereby the minimum distance was taken across a subset containing the i-th random prior from each ID. This simulated an initial deployment case, wherein everyone passes through an area once, and then a query ID passes through a second time and must be re-identified. With m as the minimum number of samples per session per ID in a given validation or test set, the process was completed m times, each time with a different subset of priors. This made use of the multiple samples per ID and maximized the number of predictions over which the metrics were computed. For a given query The performance metrics used in this study were accuracy and F1 score . When evDifferent mini-batch sizes  . Of the l height . When coAcross all models, the eight component input led to the highest mean accuracy on all but DPS,SF . Using tIn this work, DML was applied for zero-shot re-ID using a large force platform dataset that was purpose built for evaluating person re-ID systems and, thus, had the most complex set of walking conditions. Several baseline DNN architectures of varying types and complexities were implemented on four different data subsets to provide scope for future DNN design and to determine the effects of walking speed and footwear on re-ID performance. The results should be interpreted in the context of the multiple challenges introduced in the evaluation protocol. Namely, re-ID performance was evaluated in the zero-shot setting, with the additional constraints that each query sample had to be from session two and could only be compared with one random prior sample per ID from session one to generate a prediction. To illustrate the effect of constraining the number of priors, mean test accuracy of the FCNN models over seven-fold cross-validation (six sets of 29 IDs and one set of 19 IDs) was 85%  with theWhen compared to other studies on force platform-based re-ID, the present benchmark was considerably higher than the previous benchmark at many-shot re-ID conducted across measurement sessions (86% on 79 IDs), yet lower than the previous benchmarks for many-shot re-ID conducted within a single session (99%+) ,19,20,39The first main finding of this work was that models with the simplest architecture  performed the best on all four subsets. All models achieved above 97% accuracy during training, suggesting a similar level of fit in training sets. However, FCNN models required more epochs (on average) to reach this level of accuracy compared to others  ,19,20.types of footwear between sessions. Changes in heel height and sole stiffness, in particular, may pose a challenge, based on the prevalence of changes in heel height and the increased proportion of (presumably) hard-soled footwear  compared to soft-soled footwear  in the commonly mistaken group. In 2017, Derlatka compared athletic shoes to high heels and found that the accuracy of a kNN classifier only dropped considerably when high heels were encountered during testing but not training [The second main finding of this work was that footwear was the main impediment to accuracy across all models on ramework . The preThe breakdown of accuracy by walking speed revealed why accuracy was slightly higher on  samples . These fAblation studies revealed that the eight component input was generally only best on subsets with all speed categories  and presumably a smaller, yet considerable, proportion of the adult population . Data we+ years aFuture work should investigate strategies to improve performance on cross-speed and cross-footwear comparisons without compromising performance elsewhere. Based on the findings from this study, a promising avenue to explore is increasing training set size. It would be valuable to determine the independent effects of training set size  and sample distribution . With respect to sample distribution, one recommendation is to include slower and faster than preferred speed samples sparingly to better represent the speed distribution expected in most applications. Other avenues to explore include the following: time-frequency representations; alternative loss functions; more refined DNN architectures; and more advanced training methods . The independent and combined evaluation of these strategies would provide a clearer picture of the upper limit of performance. On top of this, future work should quantify the effects of gait disorders, object carriage, and partial foot contacts on both short and long-term re-ID, particularly in the challenging zero-shot setting.Force platforms are promising sensors for gait-based person re-ID because they can be applied in settings with limited or no visibility and are a rich source of kinetic information. The purpose of this work was to evaluate several baseline DNN architectures for zero-shot re-ID using a DML framework. This was done on four different subsets of a large, purpose built force platform dataset that contained significant variations in task and environmental constraints. Two-layer FCNN models slightly outperformed models with more complex architectures, achieving 85% accuracy on the entire dataset in a challenging evaluation setting. A breakdown of performance across all models revealed that re-ID accuracy declined according to the amount of change in individuals’ walking speed and footwear between measurement instances. Overall, this work confirmed the baseline utility of DML models for scalable gait-based person re-ID using force platform data.


Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are important liver enzymes in clinical settings. Their levels are known to be elevated in individuals with underlying liver diseases and those consuming hepatotoxic drugs. Serum ALT and AST levels are crucial for diagnosing and assessing liver diseases. Serum ALT is considered the most reliable and specific candidate as a disease biomarker for liver diseases. ALT and AST levels are routinely analyzed in high-risk individuals for the bioanalysis of both liver function and complications associated with drug-induced liver injury. Typically, ALT and AST require blood sampling, serum separation, and testing. Traditional methods require expensive or sophisticated equipment and trained specialists, which is often time-consuming. Therefore, developing countries have limited or no access to these methods. To address the above issues, we hypothesize that low-cost biosensing methods (paper-based assays) can be applied to the analysis of ALT and AST levels in biological fluids. The paper-based biodetection technique can semi-quantitatively measure ALT and AST from capillary finger sticks, and it will pave the way for the development of an inexpensive and rapid alternative method for the early detection and diagnosis of liver diseases. This method is expected to significantly reduce the economic burden and aid routine clinical analysis in both developed and underdeveloped countries. The development of low-cost testing platforms and their diagnostic utility will be extremely beneficial in helping millions of patients with liver disorders. Blood is one of the most widely used body fluids for monitoring the health status of the liver or diagnosing liver-associated diseases. Hepatologists and gastroenterologists rely heavily on the clinical analysis of liver enzyme levels (serum aminotransferases) to make clinical decisions. In particular, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels are crucial for diagnosing and assessing liver diseases. ALT/AST levels are also indicators of overall health status, especially in patients with obesity, metabolic syndrome, and cardiovascular disease ,2,3. ALTIt is worth noting that in developing countries, conventional pathology laboratories are expensive for diagnosing diseases and monitoring patient health. Therefore, there is great anticipation from researchers to develop simple, cheap, and easy-to-use point-of-care (POC) diagnostic tools to diagnose and monitor patients’ health status. Several research groups have reported strategies for POC diagnosis by detecting metabolites and proteins using paper, thread, and magnetic levitation ,16,17,18The design and development of low-cost, simple paper-based point-of-care testing devices to monitor health-related parameters  from blood or urine for disease diagnosis in resource-limited settings have attracted public health authorities and medical practitioners in recent years ,21. Hereex = 365 nm) and correlating it with the enzyme concentration in serum [Currently, ALT levels are frequently evaluated using spectrophotometry, chemiluminescence, colorimetric, chromatography, enzymatic kinetic reaction, and SDS-PAGE-based methods . In mostin serum . Recentlin serum . AlthougAlthough there are many ALT test kits available commercially, they still need laboratory settings for solution preparation and pipetting and may require readout instruments. In this hypothesis, we propose the use of a simple point-of-care paper-based biosensing platform for liver function testing services. 2O2, which further oxidizes 3,3′,5,5′-tetramethylbenzidine (TMB) to oxTMB by the POD-like activity of Fe@BC nanozyme, resulting in a blue-colored compound. The assay was measured in the dynamic range of 10 to 1000 U/L with a detection limit of 4 U/L [2O2) generated from the reaction between the corresponding oxidases and the substrate, which produce different colored products with the HRP as a catalyst -like activity of B-doped core–shell Fe@BC nanozyme. ALT converts L-alanine and α-ketoglutarate into L-glutamate, and the oxidation of L-glutamate by glutamate oxidase produces Hof 4 U/L . Paper-bcatalyst b. A linecatalyst c. The deA recent review by Noviana et al. detailed the design, production, and application strategies of one such device . Based o2- which reacts with blue-colored methyl green and produces a colorless compound. The device consists of two identical sheets of patterned paper with a plasma separation membrane laminated with a polyester film that prevents evaporation and protects from the environment. When a small volume (30 µL) of whole blood/serum is applied to the plasma separation membrane, blood cells are blocked by the membrane, leaving plasma to migrate from the first to the second layer. The plasma then interacts with dried reagents within the detection zone and develops a color. A colorimetric read guide can be employed for interpretation and semi-quantitative quantification because of the linear relationship between observed color density and the clinical range of ALT and AST. As shown in The transaminase paper-based device is made from a layer of patterned paper; using a wax-based printer, a heat source introduces microfluidic and hydrophilic paths within the paper, which direct the fluids to the detection zones. Within this postage-stamp-sized device  are performed: one measuring ALT and the other measuring AST. Additionally, the device has a control zone to check functionality. Each detection zone is set with a reagent that changes color in reaction with a specific enzyme. ALT is measured based on the conversion of L-alanine and alpha-ketoglutarate to pyruvate and L-glutamic acid. Pyruvate oxidase oxidizes pyruvic acid, liberating hydrogen peroxide. Subsequently, 4-aminoantipyrine and N,N-dimethylaminobenzoic acid undergo a coupling reaction in the presence of horseradish peroxidase and hydrogen peroxide to produce red-colored dye. Similarly, AST assay chemistry is used to detect AST in the sample. AST converts cysteine sulfinic acid and alpha-ketoglutaric acid to L-glutamic acid and beta-sulfinyl pyruvate. In the presence of water, beta-sulfinyl pyruvate generates SOThe paper-based assay results are compared with the gold-standard automated serum transaminase results to validate the sensitivity and specificity. We hypothesize, based on similar devices, that the overall accuracy of this device will be more than 90% for both ALT and AST enzymes. Regardless, this device semi-quantitatively detects the levels of transaminase enzymes from minute quantities of the blood, estimating liver function within 15 min, which is less than the conventional analysis time (>1 h). Since this device does not require any external equipment, patients can check their ALT levels with this paper-based instrument at home with blood collected with a finger puncher. As a result, patients can better decide whether or not to seek medical attention based on the color intensity of their blood. No such paper-based analyzer for ALT/AST measurement is commercially available to date. This is due to the asymptomatic nature of liver disease and its increasing prevalence, especially in remote and resource-limited settings, where there is an urgent need for the commercial availability of paper-based devices to monitor liver function.The results of ALT and AST colorimetric assay are quantified by comparing the color density produced by the unknown sample to known standards. Color matching using the naked eye may be complicated by many factors, such as color perception, background lighting, and color differences between the dry-printed guide and the wet-paper device. To bridge the gap, quantitative analysis of the diagnostic test was performed using additional equipment, high-resolution cameras, and scanners. To circumvent these obstacles, multiple indicators for a single analyte generate multiple colors for better visual discrimination. In one study, Dungchai et al. demonstrated semi-quantitative measurement of glucose, lactate, and uric acid in a paper-based microfluidic assay using multiple indicators. In this approach, each indicator reacted to different concentrations of peroxides and exhibited a variable range of colors for the accurate analysis of analytes. The single indicator test had an accuracy of 70%, whereas multiple indicators were over 90% accurate . The comIn conclusion, we hypothesize that paper-based assays for ALT and AST testing are rapid, extremely inexpensive, and user-friendly, and they can be used to self-monitor liver function at home and with simple fingerstick specimens. This would be more suitable for regions lacking proper healthcare infrastructure. The test would require a commercially available ALT/AST test stick, a mini finger-punch needle, and a capillary tube. This assay is extremely inexpensive compared to conventional methods and allows for the adequate follow-up of the ALT/AST range through qualitative/semi-quantitative measurements. Immediate implementation of a paper-based colorimetric assay would allow for timely treatment and prevent advanced stages of liver damage. In addition, paper-based aptasensors are considered as a potential device for future point-of-care applications. Though there are some practical limitations in the field/in vivo applications, such as nuclease digestion and rapid renal filtrations. Fast-developing modern technology would overcome these limitations in the future.


The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models’ performance discloses the most appropriate architecture for Malaysia’s specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus’s spread and minimize its effects on Malaysia’s population. Malaysia has encountered significant obstacles in containing the virus’s proliferation and addressing its repercussions. Precise forecasting of the pandemic’s dynamics is essential for effective decision-making and resource distribution by healthcare institutions, governments, and policymakers. Recently, deep learning models have shown exceptional performance in tackling various challenges in healthcare, image processing, text recognition, and natural language processing. These models have been successfully employed for numerous SARS-CoV-2 predicting tasks, making their use for predicting the pandemic’s development in Malaysia particularly relevant.In Malaysia’s context, multiple studies have used deep learning models for predicting the pandemic’s progression, but a few have used deep learning and optimization together in a single paper. The current paper has extended the deep learning models up to six models with an extended comparison of optimized and non-optimized algorithms. Further, most of the researchers have used only one to three models only. For instance,  utilizedFollowing the initial reported case in January 2020, Malaysia has enforced rigorous measures to manage the virus’s dissemination. The nation’s response has developed over time, adjusting to the pandemic’s shifting landscape. Key aspects of Malaysia’s response include lockdowns, travel restrictions, mass vaccination drives, healthcare infrastructure enhancement, and testing and tracing capabilities.Choosing suitable models and evaluation methods for SARS-COV-2 forecasting can be daunting, considering the pandemic’s intricate nature and the various factors influencing its transmission. Researchers need to contemplate several aspects, such as model intricacy, interpretability, and generalizability when creating and evaluating forecasting models. Furthermore, the rapidly changing dynamics of the pandemic demand continuous model adaptation and assessment, as models that excel at one point may become less precise as the situation progresses. Despite these challenges, advanced deep-learning models have demonstrated the potential in capturing complex data patterns and providing accurate forecasts. By comprehending the challenges and complexities of SARS-COV-2 forecasting, researchers can persist in developing and refining models, ultimately contributing to informed policymaking, strategic resource allocation, and efficient public health interventions in Malaysia. Brief Comparative Studies in Malaysia Numerous studies have carried out comparative analyses of deep learning models to assess their performance in predicting pandemic cases in Malaysia.–9 perfor–21 used These comparative studies emphasize the significance of choosing the most appropriate deep-learning model for understanding the pandemic’s progression in Malaysia. While LSTM and GRU models have proven successful in capturing long-range dependencies in time series data, CNN models have also demonstrated effectiveness in certain scenarios. The selection of a deep-learning model should be based on the specific needs and limitations of the forecasting task, as well as the available data and computational resources. Moreover, these studies underscore the importance of employing deep learning models to generate accurate and dependable forecasts, which can aid decision-makers and public health officials in mitigating the pandemic’s impact in Malaysia.We have focused on first performing different steps on the collected data and then performing tests using each deep learning model. Further, we have improved the models with Bayesian optimization to find out the difference between the optimization and without optimization of models. The study comprises data collection and preprocessing, model selection, model training and evaluation, and performance comparison.The study utilized daily SARS-COV-2 data from Malaysia, including confirmed cases, recoveries, and deaths. Data were gathered from official sources like the World Health Organization (WHO) and the Malaysia Ministry of Health. Additional data, such as government interventions, mobility patterns, and vaccination rates, were also collected to offer contextual information and enhance the models’ forecasting accuracy. The data chosen for this study was up until December 2022. Following are the descriptive statistics of data presented in The gathered data underwent preprocessing to ensure compatibility with the deep learning models. The preprocessing steps included:Data cleaning: Eliminating any missing or inconsistent values and addressing potential outliers.Feature engineering: Generating additional features, such as moving averages and growth rates, to capture relevant patterns in the data.Data normalization: Scaling the features to a standard range  to facilitate model training and convergence.Sequence generation: Converting the time series data into input-output sequences with a specified window length for model training.Exploratory data analysis was conducted to detect any anomalies and produce graphical visualizations.Several Python libraries were employed, including Numpy, Matplotlib, SkLearn, Keras, Scipy, and TensorFlow. Seven advanced deep-learning models were chosen for the comparative study:Long Short-Term Memory (LSTM)Bidirectional Long Short-Term Memory (Bi-LSTM)Convolutional Neural Network (CNN)Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM)Recurrent Neural Network (RNN)Gated Recurrent Network (GRU)Multi-layer Perceptron (MLP)These models were selected based on their proven performance in previous SARS-COV-2 forecasting studies and their capacity to capture intricate patterns and dependencies in time series data. Further Bayesian optimization was used to select the best possible hyperparameter for the dataset for each of the algorithms and then models were evaluated again to find the best accuracy. The following are initial parameters used for each algorithm presented below in It is worth noting that each algorithm works differently with a change of parameters as per the data requirement. We have further used a batch size of 1 and trained over 100 epochs for each model. These parameters were further hyper-tuned using Bayesian optimization.We have presented first the performance of each deep learning model  without optimization and then with optimization of the models. The results are summarized using tables and graphs to illustrate the performance of the models based on various evaluation metrics  and to visually compare their predictions with the actual data. The selected evaluation metrics were the best ones suited for time series data. However, many previous researchers have only used two metrics. However, as per previous studies, researchers just focused on one to three metrics only. Whereas the current evaluation focused on in-depth evaluation through five different evaluation metrics which were best suited for time series data.We were able to find out the best-performing model with optimization based on the evaluation metrics.Mean Absolute Error (MAE): For MAE, the GRU model performs the best, with the lowest MAE (0.0012), followed by RNN (0.0020) and LSTM (0.0064). The MLP model has the highest MAE (3058.42), indicating poorer performance compared to the other models.Mean Absolute Percentage Error (MAPE): The MLP model has the lowest MAPE (0.0623), followed by Bi-LSTM (2.37) and LSTM (2.40). The CNN-LSTM model has the highest MAPE (4.95596856274923), suggesting less accurate predictions compared to other models.Mean Squared Error (MSE): The GRU model outperforms the other models, with the lowest MSE (1.8719), followed by the RNN (4.943) and LSTM (4.412The MLP model has the highest MSE (18985888.755), indicating a higher error rate in its predictions compared to the other models.Root Mean Squared Error (RMSE): GRU model achieves the best performance with the lowest RMSE (0.0013), followed by the RNN (0.0022) and LSTM (0.0066). The MLP model has the highest RMSE (4357.27), which suggests less accurate predictions compared to the other models.R-squared: MLP model has the highest R-squared value (0.9982), indicating the best performance in terms of explaining the variability in the data. The GRU model follows with an R-squared value of (0.9961), while the RNN model has an R-squared value of (0 9897). The CNN-LSTM and CNN models have lower R-squared values of (0.8471), indicating that they are less effective in explaining the variability in the data compared to the other models.Best Performing Model without Optimization: Based on the above evaluation metrics and results, the GRU model consistently outperformed all other models in most of the metrics, with the lowest MAE, MSE, and RMSE values, and a high R-squared value. This indicates that the GRU model is effective in capturing the temporal dependencies in the data and providing accurate predictions for SARS-COV-2 cases in Malaysia. The RNN and LSTM models also show good performance in some metrics, but the GRU model appears to be the most effective overall.The MLP model has the highest R-squared value, suggesting it is the best at explaining the variability in the data. However, its high MAE, MSE, and RMSE values indicate a poorer performance compared with other models. Therefore, the GRU model was the best-performing model based on the evaluation metric results for predicting SARS-COV-2 cases in this context.The improved performance of different models shows that optimization techniques can significantly improve performance. With adjustment of different model parameters using the learning rate, training epochs, batch sizes, and optimizer types, significantly affects the model learning from data. We will delve deeper into the analysis by comparing each evaluation metric for better understanding.We have further worked on the model for cross-validation of results by using Bayesian optimization based on the previously used metrics. The models include LSTM, Bi-LSTM, CNN, CNN-LSTM, MLP, RNN, and GRU.MAE: In this case, the RNN with optimization has the lowest MAE at 0.00033, suggesting the highest prediction accuracy among the optimized models.MAPE: The CNN-LSTM with optimization has the lowest MAPE at 2.1747, showing the best percentage prediction accuracy.MSE: GRU with optimization has the lowest MSE at 1.4403, suggesting the best prediction accuracy in terms of squared differences.RMSE: Lower RMSE values indicate better prediction accuracy. The GRU with optimization has the lowest RMSE at 0.00037, indicating the best prediction accuracy in terms of root-squared differences.R-squared: The RNN with optimization has the highest R-squared value at 0.9996, indicating the best prediction accuracy in terms of the proportion of variance explained by the model.Summary of Best Performing Model with Optimization: Based on the evaluation metrics, the RNN with optimization appears to be the best-performing model, as it has the lowest MAE and the highest R-squared value. The GRU with optimization also demonstrates strong performance with the lowest MSE and RMSE values.When comparing the original and optimized models, we can observe improvements in the evaluation metrics for all models after optimization. Here, we present a brief comparison of each model:LSTM: The optimized LSTM model shows significant improvements in all evaluation metrics, particularly in MAE (from 0.00647 to 0.00041) and R-squared (from 0.8985 to 0.9993).Bi-LSTM: The optimized Bi-LSTM model exhibits slight improvements in MAPE (from 2.374 to 2.222) but otherwise, there’s not much improvement compared to the original model.CNN: The optimized CNN model demonstrates significant improvements in all evaluation metrics, especially in MAE (from 0.008943 to 0.00071) and R-squared (from 0.8471953812018324 to 0.9984024271812386).CNN-LSTM: The optimized CNN-LSTM model shows substantial improvements in all evaluation metrics, particularly in MAE (from 0.008943 to 0.00042) and R-squared (from 0.8471953812018324 to 0.9992621594948998).RNN: The optimized RNN model exhibits significant improvements in all evaluation metrics, especially in MAE (from 0.00200 to 0.000339) and R-squared (from 0.9897 to 0.9996).GRU: The optimized GRU model shows improvements in all evaluation metrics, with notable improvements in MAE (from 0.00123 to 0.000314) and R-squared (from 0.9961 to 0.99967).MLP: The optimized MLP model demonstrates significant improvements in all evaluation metrics, particularly in MAE (from 3058.42 to 0.00049) and R-squared (from 0.998 to 0.9990).In conclusion, the optimization process led to substantial improvements in the evaluation metrics for all models. The RNN and GRU models, in particular, have shown the best performance after optimization. This highlights the importance of optimizing deep learning models to achieve better prediction accuracy in forecasting tasks.Future research could explore alternative feature selection and preprocessing techniques to improve model performance in the Malaysian context. The study focused on filling the research gaps by forecasting SARS-COV-2 using various deep-learning methods. The models helped to accurately forecast the changing landscape of infections in Malaysia. Additionally, our study contributes to the growing body of research on using deep learning models for pandemic forecasting, which could be applied to future public health crises. Among all tested models and based on evaluation metrics, the RNN with optimization appears to be the best-performing model, as it has the lowest MAE and the highest R-squared value. The GRU with optimization also demonstrates strong performance with the lowest MSE and RMSE values.Additionally, studies could investigate the cause of the perfect scores observed for the RNN and MLP models and develop strategies to address these issues. Researchers could also consider applying other optimization methods other than Bayesian Optimization. By using the best-performing models to predict new cases, they can make more informed decisions about resource allocation, public health measures, and vaccination strategies.Accurate forecasting of SARS-COV-2 dynamics is crucial for informed policy-making and resource allocation in Malaysia. Different model’s superior performance can provide valuable insights for public health officials and decision-makers in the country. By leveraging these models, authorities can better anticipate the pandemic’s trajectory, implement timely interventions, and allocate resources efficiently to mitigate the impact of the virus on the population. The discussion section highlights the performance of the deep learning models, compares the findings with previous studies, acknowledges limitations, and suggests future research directions. It also emphasizes the implications of the study’s findings for public health policy and decision-making in Malaysia.


It is essential to collect a sufficient amount of tumor tissue for successful next-generation sequencing (NGS) analysis. In this study, we investigated the clinical risk factors for avoiding re-biopsy for NGS analysis (re-genome biopsy) in cases where a sufficient amount of tumor tissue could not be collected by bronchoscopy.We investigated the association between clinical factors and the risk of re-genome biopsy in patients who underwent transbronchial biopsy (TBB) or endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and required re-genome biopsy in cases enrolled in LC-SCRUM Asia, a prospective nationwide genome screening project in Japan. We also examined whether the frequency of re-genome biopsy decreased between the first and second halves of the enrolment period.Of the 572 eligible patients, 236 underwent TBB, and 134 underwent EBUS-TBNA. Twenty-four TBBs required re-genome biopsy, and multivariate analysis showed that the risk of re-genome biopsy was significantly increased in lesions where the tumor lesion was centrally located. In these cases, EBUS-TBNA should be utilized even if the lesion is a pulmonary lesion. However, it should be noted that even with EBUS-TBNA, lung field lesions are at a higher risk of re-canalization than mediastinal lymph node lesions. It was also found that even when tumor cells were detected in rapid on-site evaluation, a sufficient amount of tumor tissue was not always collected.For centrally located pulmonary mass lesions, EBUS-TBNA, rather than TBB, can be used to obtain tumor tissues that can be analyzed by NGS. The rapid identification of targetable driver alterations is crucial for treatment decision-making in advanced-stage lung cancer –3. MolecEGFR T790M as a resistance mutation to first- and second-generation EGFR-TKIs [Bronchoscopy is often a stressful procedure, even when general anesthesia is used . The numGFR-TKIs , 16. HowBronchoscopic biopsies for successful genetic analysis differ from diagnostic biopsies in terms of the quantity of tumor tissue specimens collected. We have made special efforts to perform these biopsies as “genome biopsies” , 10. BefThis study was conducted among patients enrolled in LC-SCRUM -Asia from 04/01/2019 to 30/11/2022 at our institution. LC-SCRUM-Asia (UMIN ID: UMIN000036871), formerly known as LC-SCRUM-Japan (UMIN ID: UMIN000010234), is a prospective, nationwide clinical and genomic screening program for lung cancer and the inclusion criteria was previously described –20. The Data on the age, sex, histopathological diagnosis, stage based on imaging studies, and genetic alteration analysis of the enrolled patients in LC-SCRUM Asia were retrospectively extracted from their electronic medical records. Based on the location, the primary lung tumor was categorized as central or peripheral using chest computed tomography (CT) imaging before treatment initiation. The criteria for categorization as central and peripheral were as follows: central location was defined as within 2 cm of the proximal bronchial tree based on the Radiation Therapy Oncology Group (RTOG) criteria, or within 2 cm of the heart, trachea, pericardium, or vertebral bodies but 1 cm away from the spinal canal based on a modification of the MD Anderson Cancer Center definition , 22. In In LC-SCRUM Asia, primarily fresh-frozen human specimens obtained by each method were submitted. A total of 100 mL of body fluid specimens were permitted. NGS was performed after the presence of tumor cells was confirmed. Sampling methods for NGS analysis, which we called “genome biopsy,” are being performed at our institution since January 2019 . The detIn lung cancer cases, a genome biopsy is a biopsy to obtain a sufficient amount of tumor tissue specimen not only to confirm the diagnosis but also to perform a successful gene panel analysis , 10. UnlROSE was performed using the modified Gill-Shorr method . For allDNA/RNA was extracted and purified using a nucleic acid extraction kit  according to the manufacturer’s protocol as previously described , 10. TheRET, ALK, ROS1, NTRK1–3, and NRG1 fusion genes; FGFR1–4 gene mutations, amplification, and fusions; MET and ERBB2 gene mutations and amplification; and BRAF, KRAS, NRAS, EGFR, and PIK3CA gene mutations [The Oncomine Comprehensive Assay v3 (OCA) was used for NGS-based analysis until December 2020, after which the Oncomine Precision Assay was employed.. In addition to these gene panels, the Amoy Master Panel was used in LC-SCRUM-Asia. Of the multiple somatic alterations analyzed in these panels, 20 mutations, which have been reported to be associated with pathogenesis of lung cancer and to have the corresponding therapeutic agents, were reported. These mutations include utations .P-values were two-sided, with P < 0.05 considered statistically significant. Pairwise comparisons were performed using Fisher’s exact test with Holm’s adjusted P-values. Logistic regression analysis was used for univariate analysis of factors related to re-genome biopsy; P < 0.05 was considered statistically significant. Statistical analyses were performed using EZR software ver 1.29  [Continuous variables underwent analysis using the Student’s t-test, while dichotomous variables were assessed using the χ2 or Fisher’s exact test, as appropriate.. All , Japan) .In total, 572 patients from our institution were enrolled in the LC-SCRUM-Asia study. The clinical characteristics of the patients are summarized in Table The list of driver alterations detected in the 572 specimens is shown in Fig. Genome biopsy was performed using TBB in 236 cases, and re-genome biopsy was deemed necessary in 24 cases, based on the assessment of the tumor content of the collected tissue Fig. . The cha9+10 lm  Table , a totalApproximately 50% of pretreatment genetic analyses in advanced or locally advanced-stage lung cancer rely on bronchoscopic biopsy , 10, in This study showed that ROSE plays a limited role in reducing the risk of re-genome biopsy. Multiple biopsies are required to obtain a sufficient amount of tissue for genome biopsy with TBB, whereas two or more biopsies are required for EBUS-TBNA , 27. It In the case of pulmonary lesions that are centrally located and externally compress the bronchus, radial EBUS may be inserted into the bronchus responsible for the lesion and produce images similar to those found within the tumor, making the distinction difficult , 30. TheEBUS-TBNA for mediastinal lymph node lesions also required re-genome biopsy in six out of 121 cases. In five out of six cases, tumor cells were present on ROSE, but sufficient tissue could not be obtained for histopathological review. In the present study, EBUS-TBNA was mainly performed using 22-gauge needles, but it is expected that the amount of tissue that can be collected will be greater with 19-gauge needles, which may also increase the success rate . There aIf tumor tissue samples cannot be obtained by bronchoscopy, genetic analysis of bronchial lavage fluid containing malignant cells may provide an alternative , 36. If This study had several limitations. First, being a single-center, retrospective study, the generalizability of the results might be limited due to biases in the TBB and EBUS-TBNA methods and the number of specimens collected. Second, imaging assessment based on the definition of central lesions was not performed in blinded independent central reviews, which does not ensure objectivity in assessing the site of lesion occupation. Third, the NGS analysis in this study was conducted as part of the LC-SCRUM Asia project, and mainly utilized fresh-frozen specimens. Fourth, to confirm the results of this study that EBUS-TBNA is more suitable for NGS analysis than TBB in lesions that centrally occupy, validation in a multicenter prospective study is needed. A common understanding of the definition of centrally location and a uniform method of approach in EBUS-TBNA is needed. Fifth, the results of this study differ slightly from those of clinical practice, as in practice, it is recommended that FFPE specimens be used to assess tumor content before NGS analysis is performed.For successful NGS-based gene panel testing, it is necessary to ensure that sufficient quantities of tumor tissue samples are collected, unlike biopsies for diagnostic purposes. It is important to use needle biopsies such as EBUS-TBNA for centrally occupying lesions, even if they are pulmonary lesions, in order to allocate a re-genome biopsy. As the success rate of genome biopsy can be improved with experience, it is expected that cryobiopsy and EBUS-TBNA using mini-forceps, which could not be examined in this study, will further enable the collection of sufficient tissue samples and reduce the risk of re-genome biopsy.


To assess the safety and the maximum tolerable dose (MTD) of iG-Tregs, we conduct a phase I–II, two-center, interventional, dose escalation (3 + 3 design), open-label study in adult patients undergoing allo-HCT from an HLA-matched sibling donor, which serves also as the donor for iG-Treg manufacturing. Herein, we present the clinical protocol with a detailed description of the study rationale and design as well as thoroughly explain every step from patient screening, product manufacturing, infusion, and participant follow-up to data collection, management, and analysis (registered EUDRACT-2021-006367-26).Regulatory T-cell (Treg) immunotherapy has emerged as a promising and highly effective strategy to combat graft-versus-host disease (GvHD) after allogeneic hematopoietic cell transplantation . Both naturally occurring Treg and induced Treg populations have been successfully evaluated in trials illustrating the feasibility, safety, and efficacy required for clinical translation. Using a non-mobilized leukapheresis, we have developed a good manufacturing practice (GMP)-compatible induced Treg product, termed iG-Tregs, that is enriched in cells expressing the potent immunosuppressive human leucocyte antigen-G molecule (HLA-G Allogeneic hematopoietic stem cell transplantation  has become a standard treatment for patients with marrow failure syndromes and hematologic malignancies such as acute leukemia. More than 20.000 allo-HCTs are performed every year in Europe with the numbers increasing . During ex vivo generation of stable induced Treg (iTreg) products of a defined phenotype that can be easily manufactured for clinical purposes. Some of these products have proceeded to phase I/II clinical studies showing the feasibility and safety of this approach with encouraging results  induces a de novo and stable expression of HLA-G, in turn, converting them to Tregs with in vitro immunosuppressive functions -grade protocol (in vitro and in vivo (manuscript in preparation). Herein, we present the protocol of the ongoing phase I/II clinical study for the evaluation of iG-Tregs against GvHD in the context of HLA-matched sibling donor allo-HCT.A very promising alternative strategy against GvHD is immunotherapy using the adoptive transfer of T cells with regulatory properties (Tregs) as a living drug aiming to avoid prolonged pharmacological immunosuppression. There is convincing evidence in pre-clinical models and promising data from early-phase clinical trials that immunotherapy with naturally occurring Tregs (nTregs) is feasible, safe, and can suppress exuberant immune activation . However results , 11. Our results . As the unctions , 14. Subprotocol . iG-Tregex vivo generated iG-Tregs in adult patients undergoing HLA-matched sibling donor HCT and the secondary objective is to assess the clinical efficacy in preventing GVHD. Patient enrollment will also be enabled for an ancillary study for the clinical evaluation of iG-Tregs in the treatment of patients with cGvHD refractory to at least two lines of treatment . The study includes a dose escalation phase I cohort to define the maximum tolerable dose (MTD) and an extension phase II cohort at the selected MTD. The primary objective is to assess the safety of Group B; .The clinical study will be conducted at the bone marrow transplantation (BMT) unit of the University General Hospital of Patras (UGHP), Rio, Greece, and HCT Unit (HCTU), “George Papanicolaou” Hospital, Thessaloniki, Greece, which are both equipped with GMP facilities and allo-HCTs are routinely performed according to the Joint Accreditation Committee ISCT-Europe & EBMT (JACIE) standards . The cur6 iG-Tregs/kg; Cohort 2: 0.5 × 106 iG-Tregs/kg; and Cohort 3: 1.5 × 106 iG-Tregs/kg), in a dose escalation fashion if no dose-limiting toxicity (DLT) is documented. The occurrence of DLT in one patient leads to the extension of the cohort to a total of six patients, whereas the presence of DLT in more than one patient of a given cohort denotes that the MTD has been surpassed. During phase ΙΙ, the enrollment of an additional eight patients and infusion of the MTD or best available dose (Cohort 4) will take place to collect further information concerning the safety profile (Recruitment of patients focuses on adults (>16 years of age) who undergo an allo-HCT from an HLA-matched sibling donor. The sibling donor will provide the starting material for iG-Treg manufacturing. All patients will be evaluated for eligibility after allo-HCT, along with the availability and eligibility of the sibling donor for leukapheresis at day (d) 30 after allo-HCT. The sample size is determined based on the “3 + 3” design . Briefly profile . CollectPrevious HLA-matched sibling allo-HCT at least 30 days before.The following criteria must be fulfilled at the initial assessment and on the day of the iG-Treg infusion:Performance status: Karnofsky ≥80%.Adequate hematopoiesis and organ function.Negative pregnancy test .3. Ability to understand and willingness to sign an informed consent form.Additional inclusion criteria for the Group B patients enrolled in the ancillary study can be found in the History of a GVHD grade ≥ II [according to the MAGIC criteria ] or admiPatients with evidence of residual disease during the final assessment.Active serious infections not responding to treatment.HBV-, HCV-, or HIV-positive patients.Administration of any investigation drug/product ≤28 days prior to iG-Treg infusion.The HLA-matched sibling donor who donated the graft for the allo-HCT of the corresponding patient will be assessed and fulfill the requirements according to institution procedures and JACIE standards to undergo leukapheresis (donor lymphocytes) without prior granulocyte colony-stimulating factor (G-CSF) infusion.Age between 16 and 75 years.Body weight > 40 kg and in good general health.The donor must be able to understand and be willing to sign an informed consent form.Negative pregnancy test .The day of graft infusion is defined as d0. Succeeding the allo-HCT, the patient serially follows carefully planned visits through the screening, follow-up, and extended follow-up periods .During this visit, the patient and the sibling donor are checked for inclusion eligibility. The consent form is signed and collected.The patient is screened to meet all eligibility criteria, and the sibling donor undergoes leukapheresis for the initiation of the iG-Tregs manufacturing. The donor undergoes steady-state leukapheresis according to institutional guidelines for less than 1 h yielding at least 60 mL of the leukapheresis product.+ content, and the transplant center will be informed of product manufacturing completion. Each iG-Tregs product comprises a unique batch of cells intended for a specific patient. The appropriate cell dose depending on the cohort is cryopreserved. Finally, the product goes through rigorous QC testing for the final release . Patients are closely monitored  for 1 h post-infusion. If any toxicity and/or adverse event  occurs, this will be recorded and managed appropriately. Participants will be instructed to avoid steroids up to 1 week after the iG-Tregs infusion.At each weekly visit, the patient is evaluated for any AE and the occurrence of aGvHD. It is emphasized that there are no drug restrictions regarding supportive care, or other treatment to prevent disease relapse while steroids should be avoided for at least up to 1 week following iG-Tregs administration. From week 13 and onward, should the patient’s status allow, a cyclosporine (CsA) tapering of a 25% weekly dose reduction can be applied until complete cessation.The patient will be evaluated at predefined time intervals according to the investigational sites’ clinical practice protocol and the observed clinical course.The current clinical trial aims to define the safety, tolerability, and MTD of the iG-Tregs (within a timeframe of 90 days following the infusion) as well as collect data regarding its clinical efficacy . The primary outcomes of this study include the (1) incidence of infusion toxicity (within 1 h after the infusion), (2) additional toxicities that may occur related to iG-Tregs infusion , and (3) AEs occurring during the first 3 weeks following iG-Treg infusion, which will be accounted for the assessment of the safety profile and tolerability of each dose during the dose escalation phase.The secondary outcomes of this study include the (1) incidence and severity of GvHD, (2) day of CsA cessation, and (3) treatment failure .Additional secondary outcomes for patients (Group B) enrolled in the ancillary study can be found in the The sponsor reserves the right to suspend enrollment or terminate the study at any time as defined in the clinical study agreement for reasons including, but not limited to, insufficient data collection, low participant enrollment rate, achievement of full enrollment, conditions imposed by the regulatory authorities, non-compliance with the clinical trial protocol, or medical reasons.p < 0.05.Given the small numbers, events will be summarized using descriptive statistics, such as frequencies and proportions. GVHD and relapse rates will be estimated as cumulative incidence curves, death in remission as a competing risk for relapse, and death without GVHD as a competing risk for GVHD. Estimates of overall survival (OS) and relapse-free survival (RFS) will be obtained by the method of Kaplan–Meier. Differences between subgroups will be compared using the Fisher exact test for categorical data and the Mann–Whitney U-test for continuous data. Statistical significance was based on All required clinical data of this trial will be collected on standardized patient follow-up forms. Confidentiality will be maintained in accordance with current clinical research principles and the General Data Protection Regulation (GDPR), and participants’ personal information will be in a pseudonymous format. The study will be monitored by the clinical trial quality assurance company . Data will be examined for protocol compliance and accuracy against source documents.in vitro and in vivo, and immune suppressive function both in vitro and in vivo will be performed. Moreover, released products will undergo T-cell receptor (TCR) repertoire next-generation sequencing (NGS)-based analysis for further characterization and in vivo tracking. Finally, serum cytokine levels will be monitored at multiple time points following product infusion.Additional research studies will be conducted in conjunction with the clinical assessment of enrolled patients. Specifically, extensive patient immunophenotyping using flow cytometry, and iG-Tregs evaluation for effector cytokine production, alloreactivity The present protocol aims to assess the clinical translation of a novel iTreg product targeting GvHD post-allo-HCT. Several groups have sought to prevent potentially lethal GvHD by employing adoptive immunotherapies either based on nTregs or iTregs , 10, 11.+CD25+ selection were accompanied by a high proportion of activated conventional T cells, which were finally co-infused along with the intended nTreg population , with the aim of promoting proper immune reconstitution. In our study, product infusion takes place at week 9 post-allo-HCT and not during the peri-transplantation period. Following MTD determination, we shall assess clinical efficacy through the ability to taper and finally cease CsA administration at an earlier time point in contrast to others, where GvHD occurrence was the main parameter. Moreover, previous work has described the effect of immunosuppressive prophylaxis—targeting calcineurin and the mammalian target of the rapamycin (mTOR) pathway—on the function of nTregs , 21. HenCollectively, the ongoing phase I/II clinical trial of iG-Tregs constitutes an innovative approach of iTreg immunotherapy in patients undergoing allo-HCT from an HLA-matched sibling donor.The trial has been registered in the EUDRACT registry prior to the inclusion of the first participant to meet the regulatory requirements (EUDRACT-2021-006367-26). After the conclusion and final analysis of the trial data, results will be submitted to a peer-reviewed medical scientific journal.The clinical trial is conducted in accordance with the ethical principles for medical research referred to in the Declaration of Helsinki by the World Medical Association and all applicable Greek and European laws and regulations regarding clinical drug trials, including the Principles of Good Clinical Practice (GCPs). Approval was obtained by jurisdictional ethics committees  and the Greek National Organization for Medicines (IS 129-22). The Investigator will submit and, where necessary, obtain approval from the aforementioned authorities for all material amendments to the original approved documents.AS: principal investigator. EY and AS: primary investigators of centers. MLy, RT, MM, KS, EY, and AS: clinical protocol design. MLy, DK, PC, NS, PP, and AP: manufacturing processes. ES, VZ, DT, GK, and MLi: clinical study procedures. MLy, DK, PC, NS, PP, RT, ES, VZ, MM, DT, GK, MLi, KS, AP, EY, and AS: project planning. MLy, DK, and AS: manuscript preparation—initial draft. All authors contributed to the article and approved the submitted version.This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call “RESEARCH—CREATE—INNOVATE (project code: T2EDK–02437).”RT, MM, and KS were employed by Pharmassist Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Thymic hyperplasia with lymphoepithelial sialadenitis-like features (LESA-like TH) is a rare form of thymic hyperplasia, characterized by a prominent expansion of the thymic medulla containing hyperplastic lymphoid follicles with germinal centers, while an almost total absence of thymic cortex. Since the first report in 2012, only a few cases of LESA-like TH have been reported in the literature to date. Due to the rarity of LESA-like TH and the tumor-like morphology, it is easy to be misdiagnosed as other common diseases of the thymus in routine practice, such as thymoma and lymphoma.Herein, we present a case report of a 52-year-old Chinese female patient with LESA-like TH, without any discomforting symptoms. Computer-tomography imaging revealed a cystic solid mass in the anterior mediastinum, with well-defined boundaries and multiple internal septa. Histologically, prominent features were florid lymphoid follicles containing germinal centers, as well as hyperplasia of thymic epithelial cells and proliferation of Hassall bodies. However, the thymic cortex rich in immature T cells was almost completely absent. Furthermore, mature plasma cells, lymphoepithelial lesions, and cholesterol clefts were frequently seen.We made a diagnosis of LESA-like TH and performed a literature review to better understand the clinicopathological features of LESA-like TH and reduce misdiagnosis. Thymic hyperplasia with lymphoepithelial sialadenitis-like features (LESA-Like TH) is a rare nonneoplastic but tumor-like thymic hyperplasia, characterized by massive lymphoid follicles with germinal centers, hyperplasia of the thymic epithelium and proliferation of the Hassall bodies, scattered lymphoepithelial lesions, and focal cystic changes. This disease was first reported in 2012 by Weissferdt et al. . The rarA 52-year-old female patient was admitted to our hospital due to a mediastinal mass found fortuitously for a month. Chest-computed tomography showed a cystic solid mass measuring 25 × 51 × 60 mm in the anterior mediastinum, with multiple septa inside. Computed tomography-enhanced scans further revealed well-defined boundaries and mild to moderate heterogeneous enhancement of the solid portion and internal septum  of the salivary glands and named them “thymic hyperplasia with lymphoepithelial sialadenitis-like features” [Macroscopically, the mass of LESA-like TH is enlarged both in size and weight. The cut surfaces are gray-white in color and medium in texture, showing multinodular architecture and focal cystic changes. Microscopically, its histological morphology shows a characteristic pattern of lobulated structures divided by fibrofatty tissue septum. There is abundant lymphofollicular hyperplasia with germinal centers, bland thymic epithelium with reticular or cord hyperplasia, and proliferation of dilated Hassall corpuscles. Mature plasma cells are scattered in the interfollicular areas, and polytypic light chain expression is confirmed by Kappa and Lambda immunohistochemical staining. In addition, the presence of lymphoepithelial lesions, cholesterol clefts and their resulting cholesterol cleft granulomas, and cystic structures lined by squamous epithelium can be observed. Immunohistochemically, epithelial markers such as pan-cytokeratin and CK19 can clearly delineate the obvious hyperplasia of thymic epithelial cells as well as Hassall corpuscles, and lymphoepithelial lesions in the medullary region. Staining for lymphocyte markers reveals a massive proliferation of mature B and T lymphocytes, while an almost complete absence of cortical areas rich in immature T cells is detected.Morphologically, LESA-like TH striking resembles LESA of the salivary gland. LESA is a component of SjÖgren’s syndrome, characterized by lobulated structure, reactive lymphoid follicles with germinal centers, hyperplasia of ducts, and lymphoepithelial lesions . It is rThe primary differential diagnosis of LESA-like TH is thymic MALT lymphoma. The median age at onset of thymic MALT lymphoma is 63 years old, about a decade later than LESA-like TH . LikewisThe thymus gland weighs about 25 g at birth and reaches a maximum weight of about 30-40 g at puberty, then begins to be gradually replaced by adipose tissue and degenerates, but does not disappear completely . “ConvenFinally, LESA-like TH needs to be differentiated from the most common epithelial tumor of the thymus (thymoma) and benign cystic lesions of the thymus. Micronodular thymoma with lymphoid stroma (MNT) shares similar features with LESA-like TH, including cyst changes, lymphoid follicles, and the paucity of immature T cells. However, MNT shows islands of spindle-shaped neoplastic epithelium surrounded by lymphoid stroma free of epithelial cells . Other sIn conclusion, we reported a Chinese patient with LESA-like TH and performed a literature review on the clinicopathological features of LESA-like TH. LESA-like TH is a rare thymic hyperplasia with tumor-like features. Due to its rare incidence, it is easy to be misdiagnosed in routine practice. Morphologically, it is characterized by a striking dilation of the thymic medullary area with lymphoid follicles containing germinal centers, hyperplastic thymic epithelial cells and Hassall bodies, lymphoepithelial lesions, plasma cell infiltration, and cystic changes, while the cortical area is mostly all absent. Therefore, LESA-like TH should be considered in the diagnosis of an anterior mediastinal mass characterized by hyperplasia of lymphoid follicular, hyperplasia of thymic epithelial cells, lymphoepithelial lesions, and the almost complete absence of the thymic cortex.