Traditionally, fresh S. japonicum flowers (SJF) and S. japonicum flowers buds (SJFB) are dried prior to further processing and use. Here, we investigated the ways in which drying techniques, including sun drying (SD), steam drying (STD), microwave drying (MD), hot air drying (HAD, 40 °C, 60 °C, 80 °C, 100 °C), and freeze drying (FD), alter the flavonoid composition of freshly-harvested SJF and SJFB. The flavonoid content of dried samples was determined by Ultra High Performance Liquid Chromatography-Diode Array Detector (UPLC-DAD). Overall, different drying techniques had significantly different effects on the RU content, ranging from 10.63 % (HAD-80 °C) to 34.13 % (HAD-100 °C) in SJF and from 18.91 % (HAD-100 °C) to 29.16 % (HAD-40 °C) and 30.53 % (SD) in SJFB. To clarify the mechanism by which drying affects the RU content of S. japonicum flowers, we studied the activity of a rutin-hydrolyzing enzyme (RHE) isolated from SJF and SJFB using multiple separation and assay methods. According to the Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) results, the apparent molecular weight of the purified RHE was approximately 38 kDa. According to UPLC-DAD, RHE catalyzes the production of quercetin (QU) from rutin (RU), but not from other flavonoid glycosides. Drying fresh SJF and SJFB at low and high temperatures can inhibit RHE activity and prevent RU hydrolysis. Therefore, subjecting freshly-harvest SJF to HAD-100 °C, and freshly-harvest SJFB to SD or HAD-40 °C, can greatly increase the RU content. In particular, HAD is viable for large-scale application due to its simplicity and industrial feasibility.

Background and Objectives: To elucidate the material basis and molecular mechanisms of the Rehmanniae Radix–Sophorae Flos (RR-SF) herb pair for psoriasis treatment, and to verify causal effects of core targets from a genetic perspective, constructing an integrated “component–target–pathway–causality” evidence chain. Materials and Methods:A four-level strategy was employed: (1) UPLC-Q-TOF/MS identified chemical constituents of the RR-SF co-decoction; (2) network pharmacology screened active compounds (OB ≥ 30%, DL ≥ 0.10), predicted targets, constructed drug–compound–target–pathway and PPI networks, and performed GO/KEGG enrichment; (3) drug-target Mendelian randomization (MR) used eQTLs of 7 core targets as exposure and psoriasis GWAS as outcome, with IVW as primary method and MR-Egger, Cochran’s Q, leave-one-out, and Steiger tests for sensitivity analysis; (4) AutoDock Vina molecular docking verified binding between active compounds and core targets. Results: 109 constituents were identified, dominated by flavonoids (32), iridoids (26), and phenylethanoid glycosides (12). Network pharmacology yielded 14 active compounds and 159 shared targets, with SRC, MAPK3, RXRA, HSP90AA1, CTNNB1, AKT1, and MAPK1 as core hubs. Key pathways included IL-17, MAPK, Th17 differentiation, VEGF, PI3K-Akt, and NF-κB. MR confirmed causal associations: MAPK1 (OR = 2.58, 95% CI 1.49–4.46, P = 7.5 × 10⁻⁴, risk), HSP90AA1 (OR = 0.42, 95% CI 0.24–0.74, P = 2.6 × 10⁻³, protective), and SRC (OR = 2.00, 95% CI 1.04–3.87, P = 0.039). MAPK1 and HSP90AA1 remained significant after Bonferroni correction. Molecular docking identified quercetin–HSP90AA1 (−9.3 kcal/mol) and aeginetic acid–RXRA (−9.1 kcal/mol) as strongest interactions. Conclusions: RR-SF exerts anti-psoriatic effects via a multi-component–multi-target–multi-pathway mode. MR validated causality of core targets, and molecular docking identified quercetin–HSP90AA1 and aeginetic acid–RXRA as key intervention strategies, providing multi-dimensional evidence for precision psoriasis treatment.

Gas chromatography–mass spectrometry (GC–MS) is widely used to separate, identify, and semi-quantify volatile and derivatizable semi-volatile constituents (mono-/sesqui-terpenoids, phenylpropanoids, fatty-acid derivatives) that carry the discriminatory chemical information needed for authentication and quality control of traditional Chinese medicinal materials (TCMMs). To frame the methodological landscape rather than merely list components, this synthesis draws on peer-reviewed GC–MS / HS‑SPME works mainly indexed in PubMed, Web of Science, and Scopus (focusing on the last decade), using searches built around keyword clusters (GC–MS, volatile oil, TCM authentication/QC, processing) and prioritizing studies that report separation conditions, pretreatment rationale, identification confidence (spectral match + retention index), and chemometric discrimination. The covered evidence shows GC–MS reliability depends less on instrument prestige than on upstream choices: pretreatment selectivity and thermal bias govern which labile markers survive; co-elution and matrix effects require explicit deconvolution/alignment; and identification confidence needs RI anchoring and, where possible, standard cross-checks, while inconsistent validation reporting and GC's intrinsic inaccessibility of strongly polar non-volatiles remain the main bottlenecks. When framed as a separation-first workflow—integrating optimized pretreatment (distillation/SPME/SFE), derivatization strategies, and multivariate modelling—GC–MS delivers verifiable chemical fingerprints to track geographical, batch, and processing-induced changes, supporting more comparable, regulation-ready QC protocols for TCMMs.