A 5:2 intermittent fasting regimen ameliorates NASH and fibrosis and blunts HCC development via hepatic PPARα and PCK1.

The role and molecular mechanisms of intermittent fasting (IF) in non-alcoholic steatohepatitis (NASH) and its transition to hepatocellular carcinoma (HCC) are unknown. Here, we identified that an IF 5:2 regimen prevents NASH development as well as ameliorates established NASH and fibrosis without affecting total calorie intake. Furthermore, the IF 5:2 regimen blunted NASH-HCC transition when applied therapeutically. The timing, length, and number of fasting cycles as well as the type of NASH diet were critical parameters determining the benefits of fasting. Combined proteome, transcriptome, and metabolome analyses identified that peroxisome-proliferator-activated receptor alpha (PPARα) and glucocorticoid-signaling-induced PCK1 act co-operatively as hepatic executors of the fasting response. In line with this, PPARα targets and PCK1 were reduced in human NASH. Notably, only fasting initiated during the active phase of mice robustly induced glucocorticoid signaling and free-fatty-acid-induced PPARα signaling. However, hepatocyte-specific glucocorticoid receptor deletion only partially abrogated the hepatic fasting response. In contrast, the combined knockdown of Ppara and Pck1 in vivo abolished the beneficial outcomes of fasting against inflammation and fibrosis. Moreover, overexpression of Pck1 alone or together with Ppara in vivo lowered hepatic triglycerides and steatosis. Our data support the notion that the IF 5:2 regimen is a promising intervention against NASH and subsequent liver cancer.

Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance.

IMPORTANCE: Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. DESIGN AND PATIENTS: In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations. RESULTS: At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). CONCLUSIONS: ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.

A simple tool for evaluation of inflammation in psoriasis: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio as markers in psoriasis patients and related murine models of psoriasis-like skin disease.

Objective parameters to quantify psoriatic inflammation are needed for interdisciplinary patient care, as well as preclinical experimental models. This study evaluates neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in psoriasis patients and five murine models of psoriasis-like skin disease based on topical imiquimod application and overexpression of IL-17A under different promotors. We performed a single-center prospective observational study in a German population, investigating psoriasis patients prior to, 4 weeks, and 16 weeks post begin of systemic anti-inflammatory therapy. Psoriasis area and severity index (PASI), blood count, and C-reactive protein (CRP) levels were attained at each timepoint. Additionally, five murine models of psoriasis-like skin disease involving five distinct experimental procedures differing in time of disease-onset and severity were investigated regarding PLR and NLR. Of 43 recruited psoriasis patients, 34 patients were followed up to 16 weeks. The cohort was 69.77% male, showing a median age of 32.0 years (range 19.0-67.0; IQR 26). The median PASI decreased from 16.35 (8.0-50.0; 10.20) to 1.6 (0-10.3; 2.56) after 16 weeks of systemic therapy. Spearman's correlation showed statistically significant positive correlation for NLR with PASI (rs = 0.27, p = 0.006), however not for PLR. NLR, but not PLR, was significantly associated with PASI in a multiple linear regression analysis including age, sex, psoriasis arthritis, and smoking. In the murine models of psoriasis-like skin disease, both NLR and PLR were significantly increased in the acute-severe models compared to controls (p < 0.001, p = 0.005, and p = 0.02, respectively), demonstrating gradually less increased values from severe-acute to mild-late-onset psoriatic phenotype. NLR was significantly associated with PASI in psoriatic patients as well as psoriatic phenotype in different murine psoriasis models. Our data warrants investigation of NLR in psoriasis patients and preclinical psoriasis models as an objective biomarker of psoriatic skin inflammation. KEY MESSAGES : NLR, but not PLR, showed a statistically significant positive correlation with Psoriasis Area and Severity Index (PASI) in our human psoriasis cohort. Both NLR and PLR were significantly increased in murine psoriasis models compared to matched controls, with gradually less increased values from severe-acute to mild-late-onset psoriatic phenotype. NLR may represent an easily available, cheap, and objective parameter to monitor psoriatic inflammation in both clinical patient routine, as well as preclinical experimental murine models.

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.

Importance: Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking. Objective: To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions. Design, Setting, and Participants: In this prospective diagnostic 2-center study, dermatologists performed skin cancer screenings using naked-eye examination and dermoscopy. Dermatologists graded suspect melanocytic lesions by the probability of malignancy (range 0-1, threshold for malignancy ≥0.5) and indicated management decisions (no action, follow-up, excision). Next, dermoscopic images of suspect lesions were assessed by a market-approved CNN, Moleanalyzer Pro (FotoFinder Systems). The CNN malignancy scores (range 0-1, threshold for malignancy ≥0.5) were transferred to dermatologists with the request to re-evaluate lesions and revise initial decisions in consideration of CNN results. Reference diagnoses were based on histopathologic examination in 125 (54.8%) lesions or, in the case of nonexcised lesions, on clinical follow-up data and expert consensus. Data were collected from October 2020 to October 2021. Main Outcomes and Measures: Primary outcome measures were diagnostic sensitivity and specificity of dermatologists alone and dermatologists cooperating with the CNN. Accuracy and receiver operator characteristic area under the curve (ROC AUC) were considered as additional measures. Results: A total of 22 dermatologists detected 228 suspect melanocytic lesions (190 nevi, 38 melanomas) in 188 patients (mean [range] age, 53.4 [19-91] years; 97 [51.6%] male patients). Diagnostic sensitivity and specificity significantly improved when dermatologists additionally integrated CNN results into decision-making (mean sensitivity from 84.2% [95% CI, 69.6%-92.6%] to 100.0% [95% CI, 90.8%-100.0%]; P = .03; mean specificity from 72.1% [95% CI, 65.3%-78.0%] to 83.7% [95% CI, 77.8%-88.3%]; P < .001; mean accuracy from 74.1% [95% CI, 68.1%-79.4%] to 86.4% [95% CI, 81.3%-90.3%]; P < .001; and mean ROC AUC from 0.895 [95% CI, 0.836-0.954] to 0.968 [95% CI, 0.948-0.988]; P = .005). In addition, the CNN alone achieved a comparable sensitivity, higher specificity, and higher diagnostic accuracy compared with dermatologists alone in classifying melanocytic lesions. Moreover, unnecessary excisions of benign nevi were reduced by 19.2%, from 104 (54.7%) of 190 benign nevi to 84 nevi when dermatologists cooperated with the CNN (P < .001). Most lesions were examined by dermatologists with 2 to 5 years (96, 42.1%) or less than 2 years of experience (78, 34.2%); others (54, 23.7%) were evaluated by dermatologists with more than 5 years of experience. Dermatologists with less dermoscopy experience cooperating with the CNN had the most diagnostic improvement compared with more experienced dermatologists. Conclusions and Relevance: In this prospective diagnostic study, these findings suggest that dermatologists may improve their performance when they cooperate with the market-approved CNN and that a broader application of this human with machine approach could be beneficial for dermatologists and patients.

Cardiovascular comorbidity in psoriasis - psoriatic inflammation is more than just skin deep.

BACKGROUND: There is a growing understanding of inflammation in psoriasis beyond its dermatological manifestation, towards systemic inflammation. Management of possible comorbidities encompassing psychological, metabolic and cardiovascular disease is recommended in national and international dermatology guidelines for treatment of psoriasis patients. Vice versa, psoriasis is being recognized as a new risk factor for cardiovascular inflammation within the cardiological community. METHODS: A review of the literature was conducted. Key points regarding epidemiological, mechanistic and management aspects were summarized and put into context for physicians treating psoriasis patients. RESULTS: Efforts are currently being made to better understand the mechanistic underpinnings of systemic inflammation within psoriatic inflammation. Studies looking to "hit two birds with one stone" regarding specifically cardiovascular comorbidities of psoriasis patients using established systemic dermatological therapies have so far provided heterogeneous data. The diagnosis of psoriasis entails preventive and therapeutic consequences regarding concomitant diseases for the individual patient. CONCLUSIONS: The knowledge of comorbidities in psoriasis calls for pronounced interdisciplinary care of psoriasis patients, to which this article highlights efforts regarding vascular inflammation and cardiovascular disease.

Reactive oxygen species produced by myeloid cells in psoriasis as a potential biofactor contributing to the development of vascular inflammation.

Psoriasis is an immune-mediated inflammatory skin disease driven by interleukin-17A (IL-17A) and associated with cardiovascular dysfunction. We used a severe psoriasis mouse model of keratinocyte IL-17A overexpression (K14-IL-17Aind/+ , IL-17Aind/+ control mice) to investigate the activity of neutrophils and a potential cellular interconnection between skin and vasculature. Levels of dermal reactive oxygen species (ROS) and their release by neutrophils were measured by lucigenin-/luminol-based assays, respectively. Quantitative RT-PCR determined neutrophilic activity and inflammation-related markers in skin and aorta. To track skin-derived immune cells, we used PhAM-K14-IL-17Aind/+ mice allowing us to mark all cells in the skin by photoconversion of a fluorescent protein to analyze their migration into spleen, aorta, and lymph nodes by flow cytometry. Compared to controls, K14-IL-17Aind/+ mice exhibited elevated ROS levels in the skin and a higher neutrophilic oxidative burst accompanied by the upregulation of several activation markers. In line with these results psoriatic mice displayed elevated expression of genes involved in neutrophil migration (e.g., Cxcl2 and S100a9) in skin and aorta. However, no direct immune cell migration from the psoriatic skin into the aortic vessel wall was observed. Neutrophils of psoriatic mice showed an activated phenotype, but no direct cellular migration from the skin to the vasculature was observed. This suggests that highly active vasculature-invading neutrophils must originate directly from the bone marrow. Hence, the skin-vasculature crosstalk in psoriasis is most likely based on the systemic effects of the autoimmune skin disease, emphasizing the importance of a systemic therapeutic approach for psoriasis patients.

Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists.

BACKGROUND: The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically 'unclear' lesions may benefit from artificial intelligence support via convolutional neural networks (CNN). METHODS: In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as 'benign', 'malignant', or 'unclear' and indicated their management decisions ('no action', 'follow-up', 'treatment/excision'). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images. RESULTS: After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as 'unclear' and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 'follow-up' or 'no action') and 43.9% of 271 truly benign cases (119 'excision'). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained 'unclear' to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01). CONCLUSIONS: Dermatologists mostly managed diagnostically 'unclear' FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions.