Predict Treatment Outcome Research Articles

Clinical, Immunologic, and Genetic Characteristics in Patients with Syndrome of Undifferentiated Recurrent Fevers (SURF).

Syndrome of Undifferentiated Recurrent Fevers (SURF) is characterized by recurrent fevers and autoinflammation without a confirmed molecular diagnosis of a Hereditary Recurrent Fever syndrome (HRF), and not fulfilling criteria for Periodic Fever, Adenitis, Pharyngitis, Aphthous stomatitis syndrome (PFAPA). The goal of this study was to characterize clinical features of SURF patients compared to PFAPA, and to analyze their cytokine signature, genetic variations, and responses to treatment. We enrolled 46 patients followed at Cincinnati Children's Hospital Medical Center. Baseline data and inflammatory cytokines were collected at enrollment, and their clinical course was followed. Cytokine analysis was performed using a cytokine multiplex assay. Many patients had specific or whole exome genetic testing. The prevalence of rash and arthralgias were higher in SURF compared to PFAPA. Pharyngitis and adenopathy were less frequent. A subset of SURF patients clustered together with elevated pro-inflammatory cytokines and more frequently required biologic therapy. Focused analysis of WES data revealed that variants of unknown clinical significance (VUCS) were frequently identified in genes implicated in B cell development, immunodeficiencies, and inflammatory bowel disease risk. Treatments for SURF patients commonly included on-demand steroids, colchicine, and anti-IL1 therapy. Our findings suggest SURF is a heterogeneous group but has distinct clinical and immunologic features from disorders like PFAPA. Patients have frequent VUCS, which may have relevance to disease pathogenesis. A subset of patients showed more inflammation and increased need for biologic use. Further research is necessary to define whether there exist distinct SURF endotypes and to better predict treatment outcomes.

Supplemental Material for Identifying Predictors of Long-Term Treatment Outcomes Using Narratives From Written Exposure Therapy and Cognitive Processing Therapy for PTSD

Supplemental Material for Identifying Predictors of Long-Term Treatment Outcomes Using Narratives From Written Exposure Therapy and Cognitive Processing Therapy for PTSD

Predictors of poor treatment outcomes among drug-resistant tuberculosis patients in Hunan province, China

BackgroundDrug-resistant tuberculosis (DR-TB) is a significant public health concern, often resulting in poor treatment outcomes. This study aims to identify predictors of poor treatment outcomes among patients with DR-TB in Hunan Province, China. MethodsA retrospective cohort study was conducted in Hunan Province using data collected between 2013 and 2018 among patients with DR-TB treatment. Univariable and multivariable parametric survival analyses were performed using a shared frailty survival model with a Weibull distribution and Gamma frailty to identify determinants of poor treatment outcomes. Adjusted hazard ratios (AHR) with a 95 % confidence interval (CI) were calculated for the best-fitted model. The goodness of fit for the model was assessed using the Cox-Snell residual test. ResultsA total of 1384 bacteriologically confirmed DR-TB patients were included in the analysis. Of these, 9.97 % (95 % CI: 8.05–11.67 %) experienced poor treatment outcomes. The hazard of poor treatment outcomes was significantly higher among patients with a history of previous TB treatment compared to those with new TB (AHR = 1.82, 95 % CI: 1.27–2.61). Additionally, each one-day delay in diagnosis was associated with a slightly increased hazard of poor treatment outcomes (AHR = 1.00034, 95 % CI:1.000041–1.00064). Patients who received medication supervision and consistent treatment follow-up (i.e., systematic management) had a significantly lower hazard of poor treatment outcomes than those without systematic management (AHR = 0.08, 95 % CI: 0.05–0.14). ConclusionA substantial proportion of DR-TB patients in Hunan Province experience poor treatment outcomes, with prior TB treatment and delays in diagnosis being key predictors. Early diagnosis and systematic management, including medication supervision and consistent follow-up, significantly reduce the risk of poor treatment outcomes. Focused interventions for previously treated TB cases are crucial to improving treatment outcomes and mitigating the risk of long-term physical sequelae among DR-TB survivors.

Focal therapy of prostate cancer: Use of artificial intelligence to define tumour volume and predict treatment outcomes

Focal therapy of prostate cancer: Use of artificial intelligence to define tumour volume and predict treatment outcomes

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

Evaluating the prognostic impact of inflammatory markers on treatment outcomes in patients with intrahepatic cholangiocarcinoma undergoing radioembolization.

Intrahepatic cholangiocarcinoma (iCCA) is a rare and aggressive malignancy with limited treatment options, often diagnosed at advanced stages. Radioembolization has emerged as a promising therapy, but its efficacy varies among patients, necessitating reliable biomarkers to predict treatment response. This study evaluates the prognostic impact of systemic inflammatory response markers on treatment outcomes in patients with iCCA undergoing radioembolization. This retrospective study included 70 patients with iCCA treated with radioembolization between January 2016 and December 2023. Inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), were measured from peripheral blood samples. Treatment response was assessed using the modified RECIST criteria, and survival analyses were performed using the Kaplan-Meier method and Cox proportional hazards regression. Patients with lower NLR, PLR, and SII values exhibited significantly higher objective response rates (P = 0.032, P = 0.016, and P = 0.001, respectively). High levels of NLR, PLR, and SII were associated with shorter overall survival (12 vs. 16 months, P = 0.007; 12 vs. 16 months, P = 0.004; and 10 vs. 22 months, P < 0.001, respectively) and progression-free survival (3 vs. 7 months, P = 0.046 for SII). Multivariate analysis identified high SII (P = 0.040), lymph node metastasis (P = 0.042), and high serum total bilirubin (P = 0.013) as significant independent prognostic factors. Systemic inflammatory markers such as NLR, PLR, and SII are valuable prognostic indicators for patients with iCCA undergoing radioembolization. These markers can aid in identifying patients likely to benefit from personalized treatment strategies, potentially improving clinical outcomes. The clinical significance of this study lies in its demonstration that systemic inflammatory markers (NLR, PLR, and SII) serve as valuable prognostic indicators for predicting treatment outcomes in patients with iCCA undergoing radioembolization, thus aiding in the identification of patients who may benefit from personalized treatment strategies and potentially improving clinical outcomes.

Utilization of Prognosis Assignment: A Cross-Sectional Survey of Military Periodontists.

Prognosis is a forecast of the likely course of a disease and its future implications on treatment outcomes. The significance of periodontal prognosis in clinical practice is evident by its influence on treatment planning, communication, and in the retrospective evaluation of treatment results. While there are multiple well-known prognosis systems reported in the periodontal dental literature, there is an absence of data on the actual use of prognosis assignment among practicing clinicians. The purpose of this study was to survey military periodontists regarding their utilization of periodontal prognosis and associated systems and to evaluate prognosis' influence on treatment planning as well as its implications on operational dental readiness. A 19-question internet survey was disseminated to military periodontists in the U.S. Air Force, Army, and Navy via their respective Periodontal Consultants to the Surgeon Generals. The survey inquired on provider-specific demographic information, the provider's favored periodontal prognosis system, when and how prognosis is applied, and on the relationship between prognosis and operational dental readiness. Fifty-five percent (n = 65 out of 120) of eligible survey participants completed the survey. Army (94.4%, n = 17) and Air Force (72.2%, n = 13) respondents preferred utilizing the Kwok and Caton periodontal prognosis system compared to 37.9% (n = 11) of Navy periodontists. Most respondents assigned prognosis at multiple time points in a patient's overall course of treatment (68%, n = 44), identified the severity of the periodontal presentation and the predictability of treatment outcomes as the most important factors involved in assigning a prognosis (82%, n = 55), and indicated that periodontal prognosis did not influence their dental readiness classification and vice versa. Kwok and Caton and McGuire periodontal prognosis systems were utilized by the majority of respondents but differed in utilization by military service branch. Respondents considered periodontal presentation severity and treatment outcome predictability the most important factors when assigning a prognosis. There was minimal influence between operational dental readiness and periodontal prognosis. Future research will evaluate prognosis system utilization in a broader population and will assess the impact of system standardization in education and clinical practice.

Prospective multicenter study of camrelizumab in real-world settings for asian patients with esophageal squamous cell carcinoma

BackgroundIn this study, we aimed to evaluate the real-world efficacy and safety of camrelizumab and identify clinicolaboratory factors that predict treatment outcomes in patients with unresectable advanced, recurrent, or metastatic esophageal squamous cell carcinoma (ESCC) receiving camrelizumab.MethodsHerein, 174 patients with unresectable advanced, recurrent, or metastatic ESCC treated with camrelizumab monotherapy (n = 30), camrelizumab + chemotherapy (CT; n = 91), and camrelizumab + radiotherapy (RT; n = 53) between October 1, 2019 and October 1, 2022 were included.ResultsThe median follow-up time was 20 months (range, 1–34 months). The median progression-free survival (PFS) and overall survival (OS) of the whole cohort were 8 months [95% confidence interval (CI), 6.5–9.5 months] and 14 months (95% CI, 11.2–16.8 months), respectively. After multivariate analysis, receiving > 4 cycles of camrelizumab was identified as an independent predictor of better PFS [hazard ratio (HR), 0.56; 95% CI, 0.38–0.827; P = 0.004] and OS (HR, 0.532; 95% CI, 0.341–0.83; P = 0.005). An intermediate-to-poor lung immune prognostic index (LIPI) was identified as an independent predictor of worse PFS (HR, 1.505; 95% CI, 1.032–2.196; P = 0.034) and OS (HR, 1.657; 95% CI, 1.094–2.51; P = 0.017). The disease control rate of patients in the camrelizumab monotherapy group, camrelizumab + CT group, and camrelizumab + RT group was 92.3% (95% CI, 74.9–99.1%), 90.6% (95% CI, 82.3–95.9%), and 96.1% (95% CI, 86.8–99.5%), respectively. The treatment-related adverse events (AEs) of grade 3 or higher were reported in 67 patients (38.5%). The most common treatment-related AEs were decreased neutrophil count (23.0%), decreased white blood cell count (19.5%), anemia (7.5%), and pneumonitis (4.6%). One patient (0.6%) died from a treatment-related AE of immune checkpoint inhibitor-induced myocarditis.ConclusionCamrelizumab was safe and effective as both monotherapy and part of a combination therapy. Longer PFS and OS were associated with receiving > 4 cycles of camrelizumab and having a good LIPI. LIPI can be used as a prognostic biomarker for ESCC patients receiving camrelizumab + RT.Trial registrationClinicalTrial.gov Identifier: CHICTR2000039499. Registered: 19th October 2020.

Functional gradient dysfunction in drug-naïve first-episode schizophrenia and its correlation with specific transcriptional patterns and treatment predictions

Abstract Background First-episode schizophrenia (FES) is a progressive psychiatric disorder influenced by genetics, environmental factors, and brain function. The functional gradient deficits of drug-naïve FES and its relationship to gene expression profiles and treatment outcomes are unknown. Methods In this study, we engaged a cohort of 116 FES and 100 healthy controls (HC), aged 7 to 30 years, including 15 FES over an 8-week antipsychotic medication regimen. Our examination focused on primary-to-transmodal alterations in voxel-based connection gradients in FES. Then, we employed network topology, Neurosynth, postmortem gene expression, and support vector regression to evaluate integration and segregation functions, meta-analytic cognitive terms, transcriptional patterns, and treatment predictions. Results FES displayed diminished global connectome gradients (Cohen's d = 0.32–0.57) correlated with compensatory integration and segregation functions (Cohen's d = 0.31–0.36). Predominant alterations were observed in the default (67.6%) and sensorimotor (21.9%) network, related to high-order cognitive functions. Furthermore, we identified notable overlaps between partial least squares (PLS1) weighted genes and dysregulated genes in other psychiatric conditions. Genes linked with gradient alterations were enriched in synaptic signaling, neurodevelopment process, specific astrocytes, cortical layers (layer II and IV), and developmental phases from late/mid fetal to young adulthood. Additionally, the onset age influenced the severity of FES, with discernible differences in connection gradients between minor- and adult-FES. Moreover, the connectivity gradients of FES at baseline significantly predicted treatment outcomes. Conclusions These results offer significant theoretical foundations for elucidating the intricate interplay between macroscopic functional connection gradient changes and microscopic transcriptional patterns during the onset and progression of FES.

Factors associated with poor asthma control in children: A prediction model.

Inhaled corticosteroids (ICS) are the first-line therapy for pediatric asthma. However, very few studies have developed simple tools for predicting treatment outcomes in pediatric asthma. This study aimed to construct a predictive model for poor asthma control in children after 6 months of ICS therapy. This retrospective study included children with asthma, aged 6-15 years, who received ICS with complete follow-up for 6 months. The potential factors associated with poor asthma control were also assessed. Poor control was considered if the child had partial or uncontrolled symptoms according to the Global Initiative for Asthma guidelines. Among the 165 eligible children, 33 (20%) had poor symptom control. The factors associated with poor control were a history of more than four exacerbations in the 12 months before ICS treatment (odds ratio [OR], 3.39 [1.06, 10.83]), the presence of moderate to severe allergic rhinitis symptoms at the 6-month follow-up visit (OR, 21.93 [2.97, 162.05]), and poor adherence to asthma medications (OR, 4.16 [1.32, 13.12]). By incorporating these factors, a model for predicting poorly controlled asthma was constructed and converted into a nomogram with a total score of 200, with prediction risk ranging from 0 to 100%. The area under the receiver operating characteristic curve of the developed model was 0.737, indicating a moderate performance level. We developed a predictive tool for poor asthma control. The model has a good discriminatory ability and is simple to use, which could facilitate the individualized management of children with asthma.

Anti-VEGF treatment outcome prediction based on optical coherence tomography images in neovascular age-related macular degeneration using a deep neural network.

Age-related macular degeneration (AMD) is a major cause of blindness in developed countries, and the number of affected patients is increasing worldwide. Intravitreal injections of anti-vascular endothelial growth factor (VEGF) are the standard therapy for neovascular AMD (nAMD), and optical coherence tomography (OCT) is a crucial tool for evaluating the anatomical condition of the macula. However, OCT has limitations in accurately predicting the degree of functional and morphological improvement following intravitreal injections. Artificial intelligence (AI) has been proposed as a tool for predicting the treatment response of nAMD based on OCT biomarkers. Our study focuses on the development and assessment of an AI model utilizing the DenseNet201 algorithm. The model aims to predict anatomical improvement based on OCT images before, and during anti-VEGF therapy. The training process involves two scenarios: (1) using only preinjection OCT images and (2) utilizing both OCT images before and during anti-VEGF therapy for model training. The outcomes of our investigation, involving 2068 images from a cohort of 517 Korean patients diagnosed with nAMD, indicate that the AI model we introduced surpassed the predictive performance of ophthalmologists. The model exhibited a sensitivity of 0.915, specificity of 0.426, and accuracy of 0.820. Notably, its predictive capabilities were further enhanced with the inclusion of additional OCT images taken after the first and second injections during the loading phase. The treatment prediction performance of the model was the highest when using all input modalities (before injection, and after the first and second injections) and concatenation-based fusion layers. This study highlights the potential of AI in assisting individualized and tailored nAMD treatment.

Integration of cone beam computed tomography for interdisciplinary diagnosis and treatment planning

Cone-beam computed tomography (CBCT) has revolutionized dental imaging by providing precise three-dimensional visualization of hard tissue structures with minimal radiation exposure compared to conventional CT. This review explores the integration of CBCT in interdisciplinary diagnosis and treatment planning across various fields of dentistry. CBCT offers versatile applications including assessment of impacted teeth, airway analysis, temporomandibular joint evaluation, detection of jaw pathologies, and precise diagnosis of dento-maxillofacial fractures. Its ability to generate static and rotational 3D images aids in surgical planning and treatment outcome prediction. Furthermore, CBCT plays a crucial role in orthodontics for localization of impacted teeth and evaluation of craniofacial anomalies such as cleft lip and palate. In periodontics and implantology, CBCT facilitates detailed assessment of periodontal structures, bone defects, and precise implant placement. Additionally, CBCT assists in maxillofacial prosthetics by enabling virtual surgical planning and custom prosthetic fabrication. This paper underscores CBCT as an indispensable tool in modern dental practice, enhancing diagnostic accuracy, treatment precision, and patient outcomes across diverse clinical scenarios.

Gender differences in periodontal pocket microflora in patients with chronic generalized periodontitis

Background: Periodontal disease, the primary cause of tooth loss, is a critical public health concern with a considerable impact on quality of life. It is of interest to explore gender differences in the course of inflammatory periodontal diseases, as well as the prospects of their personalized treatment and prevention in males and females. Aim: To assess the dental health and quantitative composition of periodontal pocket microflora in periodontitis patients, taking into account gender differences. Materials and Methods: The study included 39 patients with moderate chronic generalized periodontitis aged 40–60 years. The diagnosis was made using both primary and secondary dental diagnostic tools. The study had two groups: males (41%, n=16) and females (59%, n=23). The following parameters were assessed: oral hygiene index (OHI-S), severity of gingival inflammation (PMA), and quantitative composition of periodontal pocket microflora (DENTOSCREEN, “Lytech” Co. Ltd., Russia), taking into account gender differences. The study findings were statistically processed using Microsoft Office Excel 2016 with Data Analysis and AtteStat 12.0.5 add-ins. The Shapiro–Wilk test was used. The data are presented as median and interquartile range: Me [Q1; Q3]. The frequency of microorganisms is presented as percentage of the mean and standard error of the mean. The confidence interval was calculated using the Wald method, according to the formula for proportions and frequencies. For small values, the Wald method with Agresti–Coull correction was used. Samples were compared using the following tests: the Mann–Whitney U test was used for quantitative data and Fisher’s angular transformation (φ*emp) for frequency (nominal) attributes. Significant differences were defined as a probability 95% (p 0.05). Results: The quantitative composition of periodontal pocket microflora in patients with moderate periodontitis revealed significant gender differences, as demonstrated by an increase in the count of Fusobacterium nucleatum and Prevotella intermedia, as well as the frequency of Porphyromonas gingivalis, in males compared to females (φ*emp=1.736; p 0.05). Dental health indices confirmed the hypothesis of significant differences between males and females in terms of plaque index (OHI-S) and РМА. In males, poor oral hygiene was reported in 93.7% cases, which was significantly higher (φ*emp=2.04; p 0.05) than in females (69.6%). Moderate gingival inflammation was more prevalent in males, while mild gingival inflammation was more prevalent in females (p 0.05). Conclusion: The study findings emphasize the importance of oral hygiene in the prevention of chronic periodontitis and can be used to improve complex treatment algorithms for patients with periodontal diseases. This includes more frequent dental check-ups and professional oral care sessions, as well as oral hygiene training and monitoring, in males compared to females. Diagnosis by polymerase chain reaction allows for the identification and quantitative determination of DNA from periodontopathogenic bacteria at low concentrations, as well as the justification of treatment algorithms and prediction of treatment outcomes.

EyeMatics-Multicenter data evaluation of real-world data with interoperable medical informatics

The evaluation of real-world data (RWD) enables insights to be gained from awide range of patient data collected in routine clinical practice. In addition, multicenter analyses represent abroad and representative patient population and have the potential to capture the actual treatment situation. As abasis for this, the definition of datasets and an infrastructure for data exchange is necessary. Data integration centers (DIC) have already been established at (university) hospitals throughout Germany in order to extract RWD for scientific analyses from the various source systems and integrate them into research-compatible data infrastructures. The project described here aims to demonstrate the added value of this data integration using a case of application in ophthalmology, defining acore dataset as an ophthalmology extension module and establishing across-site data exchange infrastructure. As afirst step, the treatment success of eye diseases treated with intravitreal injection (IVI) should be improved. To achieve this goal adashboard for clinical data is provided that clearly visualizes the merged data. Furthermore, algorithms will be developed to identify new imaging biomarkers that can be used for treatment monitoring and predict treatment outcomes.

THE RELATIONSHIP BETWEEN RETINAL PERFUSION AND FUNCTIONAL RECOVERING OF FOVEA IN DIFFERENT TYPES OF SURGICAL CLOSURE OF FULL-THICKNESS MACULAR HOLES

Rationale: The effect of retinal perfusion on functional recovery of the fovea, as well as the value of quantitative perfusion parameters as predictors of functional outcome in the surgical treatment of full-thickness macular holes (FTMH) remains poorly understood.Objective: To study the relationship between changes in retinal perfusion and functional parameters of the fovea in the postoperative period at 1 and 2 types of FTMH closure.Methods: A prospective study including 93 patients (93 eyes) with idiopathic FTMH stage 2-4 according to D. Gass after surgical treatment. In subgroups with type 1 and type 2 of FTMH closure, changes in the foveal avascular zone (FAZ) area and vessel density in the superficial and deep capillary plexuses (SCP and DCP) in the fovea, changes in BCVA, retinal sensitivity (RS) at the fixation point, and P1 amplitude in the fovea were studied. Studies were performed preoperatively, 1 and 6 months after surgery. A significant correlation was found between the changes in BCVA, RS at the fixation point, P1 amplitude in the fovea, and changes in vessel density in the SCP and DCP, which was more pronounced in the period from 1 to 6 months after surgery. The correlation of the changes in the parameters is higher in type 2 of FTMH closure than in type 1 of FTMH closure.Results:. Subgroups with type 1 and type 2 FTMH closure included 67 and 26 eyes, respectively. Type 2 FTMH closure showed lower vascular density in the DCP before (p=0.02) and 6 months after surgery (p=0.03), lower BCVA before surgery (p=0.03), 1 and 6 months after surgery (p &lt;0.001), lower RS at the fixation point before and after surgery (p&lt;0.002).Conclusion. In type 2 macular hole closure, postoperative changes in retinal perfusion have a more pronounced impact on functional recovery of the fovea, especially in the late postoperative period.

Application of platelet transcriptomics for assessing treatment effectiveness and predicting long-term platelet counts recovery in aplastic anemia.

Aplastic anemia (AA) is a bone marrow failure disease for which the means of assessing and predicting the therapeutic effectiveness are still relatively limited. Thrombocytopenia is often the earliest and most severe symptom in patients newly diagnosed with AA (Dx-AA). While clinical consideration is usually given to the quantitative changes in platelets during treatment, there is little focus on the resolution of the molecular characteristics of platelets in AA. To investigate the changes in platelet molecular characteristics throughout the treatment process of AA, and to explore the use of transcriptomics for monitoring and predicting treatment outcomes. We comprehensively analyzed platelet transcriptomic changes in AA patients at initial diagnosis and different stages of treatment effectiveness using bulk transcriptome sequencing. Genes associated with cell proliferation, erythroid function, and amino acid transport were elevated in Dx-AA. Conversely, genes linked to histones, thrombosis, mitochondrial energy metabolism, and signaling pathways were significantly downregulated. 60.6% of the differentially expressed genes were substantially restored following complete remission. Furthermore, through the examination of longitudinal samples, we identified recovery ascending genes (RAG) that could serve as viable biomarkers for assessing treatment effectiveness in AA. Besides, we observed that higher expression levels of RAG may predict superior long-term platelet counts recovery six months in advance in patients with partial response. The platelet transcriptome undergoes profound changes and can serve as a potential indicator for assessing treatment effectiveness and predicting long-term platelet counts recovery in AA.

Role of Th1/Th2 cytokine balance in predicting treatment outcomes and disease severity in tuberculosis

Tuberculosis (TB) is an age-old disease that remains a significant global public health issue. The protective response to Mycobacterium tuberculosis (MTB) is a complex and multifaceted process involving several components of the immune system, primarily driven by the cooperation between macrophages and T-cell populations. Various animal and human studies have well established the influential roles that cytokines and chemokines play in determining the outcome of MTB infection. The study focused on assessing the influence of Th1 and Th2 responses in tuberculosis by examining the current cytokine profiles in TB patients, emphasizing Th1 and Th2 cytokines, and comparing these profiles with those of patients undergoing treatment and a control group. Additionally, the relationship between cytokine status and the patients’ sex and age was assessed. The analysis of Th1/Th2 cytokines revealed a dichotomy between untreated and treated conditions. The results showed that untreated individuals suffered from a Th1 cytokine deficiency. However, this condition was reversed following the administration of anti-TB antibiotics, with patients who received these drugs showing a shift towards a protective Th1 cytokine profile. Cytokines play a decisive role in various infectious diseases, and this study confirms that TB is among them. The findings from this research could pave the way for novel diagnostic and therapeutic approaches in tuberculosis research.

Predictors of Treatment Outcome in Persons With Anorexia Nervosa: On the Practice of Regressing Body Mass Index at the End of Treatment on Body Mass Index at Baseline.

It is often stated that a higher body mass index (BMI) at the beginning of treatment predicts a better weight outcome at the end of treatment in persons with anorexia nervosa (AN). However, this interpretation is based on the between-persons relationship of BMI at the two measurements, which primarily reflects the fact that the rank-ordering of persons according to their BMI is quite stable over time. In contrast, a lower BMI at baseline is related to a larger BMI change, which primarily reflects the fact that the variance of BMI at the end of treatment is larger than that at baseline. This study aimed to demonstrate these relationships empirically and caution against interpreting BMI at baseline as a predictor of BMI at discharge or BMI change. Changes of BMI from admission to discharge were analyzed based on 4863 persons with AN (97% female) who received inpatient treatment between 2015 and 2024. BMI at admission was positively related to BMI at discharge (r = 0.55) but negatively related to BMI change from admission to discharge (r = -0.39). While it is true that a higher BMI at baseline is associated with a higher BMI at the end of treatment, a lower BMI at baseline is actually related to a larger weight gain during treatment. Yet, concluding that the treatment is more effective for patients with low or high BMI at baseline would be incorrect in either case, as the independent and dependent variables are the same variables measured at different time points.

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data. This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge. Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field. AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics. Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care. Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Repetitive Negative Thinking as a Predictor of Cognitive Behavioral Therapy Outcomes in a Naturalistic Outpatient Treatment

Abstract Background Previous research has reported associations between various forms of repetitive negative thinking (RNT; e.g., rumination, worry, and the content-independent process of RNT) and the onset and maintenance of psychological disorders. Additionally, reductions in RNT are linked to improved treatment outcomes, primarily based on findings from randomized controlled trials. This study examined RNT in the context of outpatient cognitive behavioral therapy (CBT) in routine clinical care. Methods We investigated the predictive value of RNT in a sample of 506 patients from an outpatient clinic and postgraduate training center for CBT, all of whom received CBT for various mental health conditions. Of these, 256 (50.6%) presented with depressive disorder, and we repeated our analyses using only this subsample. We assessed content-independent RNT, brooding, and worrying at pre- and post-assessment. General psychopathology and depression were assessed at pre- and post-assessment as well as at 6- and 12-month follow-up. Results There were significant reductions in general psychopathology and depression between pre-assessment and all subsequent assessments. All forms of RNT showed significant reductions from pre- to post-assessment. These reductions predicted general psychopathology and depression at post-assessment. At follow-up, findings were mixed, with only some forms of RNT being significantly associated with treatment outcome. Conclusions Naturalistic CBT reduced general psychopathology and depression, as well as content-independent RNT, rumination, and worrying. Change in all forms of RNT predicted reductions in general psychopathology and depression at post-assessment. This study highlights the relevance of addressing different forms of RNT in therapeutic interventions.