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How psychotic symptoms, depressive symptoms, cognitive deficits, and functional impairment may interact with one another in schizophrenia or bipolar disorder is unclear.
Methods
This study explored these interactions in a discovery sample of 339 Chinese, of whom 146 had first-episode schizophrenia and 193 had bipolar disorder. Psychotic symptoms were assessed using the Positive and Negative Symptom Scale; depressive symptoms, using the Hamilton Depression Rating Scale; cognitive deficits, using tests of processing speed, executive function, and logical memory; and functional impairment, using clinical assessments. Network models connecting the four types of variables were developed and compared between men and women and between disorders. Potential causal relationships among the variables were explored through directed acyclic graphing. The results in the discovery sample were compared to those obtained for a validation sample of 235 Chinese, of whom 138 had chronic schizophrenia and 97 had bipolar disorder.
Results
In the discovery and validation cohorts, schizophrenia and bipolar disorder showed similar networks of associations, in which the central hubs included ‘disorganized’ symptoms, depressive symptoms, and deficits in processing speed during the digital symbol substitution test. Directed acyclic graphing suggested that disorganized symptoms were upstream drivers of cognitive impairment and functional decline, while core depressive symptoms (e.g. low mood) drove somatic and anxiety symptoms.
Conclusions
Our study advocates for transdiagnostic, network-informed strategies prioritizing the mitigation of disorganization and depressive symptoms to disrupt symptom cascades and improve functional outcomes in schizophrenia and bipolar disorder.
Schizophrenia is a severe psychiatric disorder characterised by positive symptoms, such as hallucinations and delusions, which are linked to dysregulated striatal connectivity. Although traditional models highlight the limbic striatum’s role in salience processing, emerging evidence suggests that the associative striatum, critical for cognitive control and habit formation, also plays a significant role. However, the structural connectivity underlying striatal subregions and its relationship to symptom severity and treatment response remains poorly understood.
Aims
This study aimed to investigate the structural connectivity of striatal subregions in first-episode schizophrenia (FE-SCZ) patients and to evaluate its association with positive symptoms and changes following antipsychotic treatment.
Method
We recruited 80 FE-SCZ patients and 80 healthy controls who underwent diffusion tensor imaging probabilistic tractography to assess white matter tract strength between the striatum and ten cortical targets. Longitudinal analysis was conducted in patients at baseline (within 2 weeks of initial antipsychotic exposure) and after ongoing treatment to evaluate changes in connectivity and their relationship to symptom improvement.
Results
FE-SCZ patients exhibited reduced connectivity between the dorsolateral prefrontal cortex (dlPFC) and associative striatum and increased connectivity between the anterior cingulate cortex (ACC) and associative striatum compared to controls. Longitudinal analysis revealed that antipsychotic treatment increased dlPFC–associative striatum connectivity and decreased ACC–associative striatum connectivity, which correlated with reductions in positive symptom severity.
Conclusions
These findings highlight the critical role of striatal subregions in the pathophysiology of schizophrenia, emphasising the associative striatum’s involvement in cognitive control and salience attribution. Changes in striatal connectivity after continued antipsychotic therapy may serve as a biomarker for symptom improvement, advancing our understanding of schizophrenia and guiding future therapeutic strategies.
The Hector Galaxy Survey is a new optical integral field spectroscopy (IFS) survey currently using the Anglo-Australian Telescope to observe up to 15 000 galaxies at low redshift ($z \lt 0.1$). The Hector instrument employs 21 optical fibre bundles feeding into two double-beam spectrographs, AAOmega and the new Spector spectrograph, to enable wide-field multi-object IFS observations of galaxies. To efficiently process the survey data, we adopt the data reduction pipeline developed for the SAMI Galaxy Survey, with significant updates to accommodate Hector’s dual-spectrograph system. These enhancements address key differences in spectral resolution and other instrumental characteristics relative to SAMI and are specifically optimised for Hector’s unique configuration. We introduce a two-dimensional arc fitting approach that reduces the root-mean-square (RMS) velocity scatter by a factor of 1.2–3.4 compared to fitting arc lines independently for each fibre. The pipeline also incorporates detailed modelling of chromatic optical distortion in the wide-field corrector, to account for wavelength-dependent spatial shifts across the focal plane. We assess data quality through a series of validation tests, including wavelength solution accuracy (1.2–2.7 km s$^{-1}$ RMS), spectral resolution (FWHM of 1.2–1.4 Å for Spector), throughput characterisation, astrometric precision ($\lesssim$ 0.03 arcsec median offset), sky subtraction residuals (1–1.6% median continuum residual), and flux calibration stability (4% systematic offset when compared to Legacy Survey fluxes). We demonstrate that Hector delivers high-fidelity, science-ready datasets, supporting robust measurements of galaxy kinematics, stellar populations, and emission-line properties and provide examples. Additionally, we address systematic uncertainties identified during the data processing and propose future improvements to enhance the precision and reliability of upcoming data releases. This work establishes a robust data reduction framework for Hector, delivering high-quality data products that support a broad range of extragalactic studies.
Previous studies highlighted the health benefits of coffee and tea, but they only focused on the comparisons between different consumptions. Consequently, the association estimate lacked a clear interpretation, as the substitution of beverages and distribution of doses were not explicitly prescribed. We focused on the ‘relative association’ to ascertain the optimal consumption strategy (including total intake and optimal allocation strategy) for coffee, tea and plain water associated with decreased mortality. Self-reported coffee, tea and plain water intake were used from the UK Biobank. Within a compositional data analysis framework, a multivariate Cox model was used to assess the relative associations after adjusting for a range of potential confounders. The lower mortality risk was observed with at least approximately 7–8 drinks/d of total consumption. When the total intake > 4 drinks/d, substituting plain water with coffee or tea was linked to reduced mortality; nevertheless, the benefit was not seen for ≤ 4 drinks/d. Besides, a balanced consumption of coffee and tea (roughly a ratio of 2:3) associated with the lowest hazard ratios of 0·55 (95 % CI 0·47, 0·64) for all-cause mortality, 0·59 (95 % CI 0·48, 0·72) for cancer mortality, 0·69 (95 % CI 0·49, 0·99) for CVD mortality, 0·28 (95 % CI 0·15, 0·52) for respiratory disease mortality and 0·35 (95 % CI 0·15, 0·82) for digestive disease mortality than other combinations. These results highlight the importance of the rational combination of coffee, tea and plain water, with particular emphasis on ensuring adequate total intake, offering more comprehensive and explicit guidance for individuals.
Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.
Methods:
We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.
Results:
The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.
Conclusion:
This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.
Compelling evidence claims that gut microbial dysbiosis may be causally associated with major depressive disorder (MDD), with a particular focus on Alistipes. However, little is known about the potential microbiota–gut–brain axis mechanisms by which Alistipes exerts its pathogenic effects in MDD.
Methods
We collected data from 16S rDNA amplicon sequencing, untargeted metabolomics, and multimodal brain magnetic resonance imaging from 111 MDD patients and 102 healthy controls. We used multistage linked analyses, including group comparisons, correlation analyses, and mediation analyses, to explore the relationships between the gut microbiome (Alistipes), fecal metabolome, brain imaging, and behaviors in MDD.
Results
Gut microbiome analysis demonstrated that MDD patients had a higher abundance of Alistipes relative to controls. Partial least squares regression revealed that the increased Alistipes was significantly associated with fecal metabolome in MDD, involving a range of metabolites mainly enriched for amino acid, vitamin B, and bile acid metabolism pathways. Correlation analyses showed that the Alistipes-related metabolites were associated with a wide array of brain imaging measures involving gray matter morphology, spontaneous brain function, and white matter integrity, among which the brain functional measures were, in turn, associated with affective symptoms (anxiety and anhedonia) and cognition (sustained attention) in MDD. Of more importance, further mediation analyses identified multiple significant mediation pathways where the brain functional measures in the visual cortex mediated the associations of metabolites with behavioral deficits.
Conclusion
Our findings provide a proof of concept that Alistipes and its related metabolites play a critical role in the pathophysiology of MDD through the microbiota–gut–brain axis.
The cyclicity and Koblitz conjectures ask about the distribution of primes of cyclic and prime-order reduction, respectively, for elliptic curves over $\mathbb {Q}$. In 1976, Serre gave a conditional proof of the cyclicity conjecture, but the Koblitz conjecture (refined by Zywina in 2011) remains open. The conjectures are now known unconditionally “on average” due to work of Banks–Shparlinski and Balog–Cojocaru–David. Recently, there has been a growing interest in the cyclicity conjecture for primes in arithmetic progressions (AP), with relevant work by Akbal–Güloğlu and Wong. In this article, we adapt Zywina’s method to formulate the Koblitz conjecture for AP and refine a theorem of Jones to establish results on the moments of the constants in both the cyclicity and Koblitz conjectures for AP. In doing so, we uncover a somewhat counterintuitive phenomenon: On average, these two constants are oppositely biased over congruence classes. Finally, in an accompanying repository, we give Magma code for computing the constants discussed in this article.
Triceps skinfold thickness (TSF) is a surrogate marker of subcutaneous fat. Evidence is limited about the association of sex-specific TSF with the risk of all-cause mortality among maintenance hemodialysis (MHD) patients. We aimed to investigate the longitudinal relationship of TSF with all-cause mortality among MHD patients. A multicenter prospective cohort study was performed in 1034 patients undergoing MHD. The primary outcome was all-cause mortality. Multivariable Cox proportional hazards models were used to evaluate the association of TSF with the risk of mortality. The mean (standard deviation) age of the study population was 54.1 (15.1) years. 599 (57.9%) of the participants were male. The median (interquartile range) of TSF was 9.7 (6.3–13.3 mm) in males and 12.7 (10.0–18.0 mm) in females. Over a median follow up of 4.4 years (interquartile range, 2.4-7.9 years), there were 548 (53.0%) deaths. When TSF was assessed as sex-specific quartiles, compared with those in quartile 1, the adjusted HRs (95%CIs) of all-cause mortality in quartile 2, quartile 3 and quartile 4 were 0.93 (0.73, 1.19), 0.75 (0.58, 0.97) and 0.69 (0.52, 0.92), respectively (P for trend =0.005). Moreover, when analyzed by sex, increased TSF (≥9.7 mm for males and ≥18mm for females) was significantly associated with a reduced risk of all-cause mortality (quartile 3-4 vs. quartile 1-2; HR, 0.70; 95%CI: 0.55, 0.90 in males; quartile 4 vs. Quartile 1-3; HR, 0.69; 95%CI: 0.48, 1.00 in females). In conclusion, high TSF was significantly associated with lower risk of all-cause mortality in MHD patients.
This paper presents an investigation of the secondary saturation characteristics of a HfTe2 saturable absorber. Pulse energies of 5.85 and 7.4 mJ were demonstrated with a high-order Hermite–Gaussian (HG) laser and a vortex laser, respectively, using alexandrite as the gain medium. To the best of our knowledge, these are the highest pulse energies directly generated with HG and vortex lasers. To broaden the applications of high-energy pulsed HG and vortex lasers, wavelength tuning in the region of 40 nm was achieved using an etalon.
This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.
Knowledge of the critical periods of crop–weed competition is crucial for designing weed management strategies in cropping systems. In the Lower Yangtze Valley, China, field experiments were conducted in 2011 and 2012 to study the effect of interference from mixed natural weed populations on cotton growth and yield and to determine the critical period for weed control (CPWC) in direct-seeded cotton. Two treatments were applied: allowing weeds to infest the crop or keeping plots weed-free for increasing periods (0, 1, 2, 4, 6, 8, 10, 12, 14, and 20 wk) after crop emergence. The results show that mixed natural weed infestations led to 35- to 55-cm shorter cotton plants with stem diameters 10 to 13 mm smaller throughout the season, fitting well with modified Gompertz and logistic models, respectively. Season-long competition with weeds reduced the number of fruit branches per plant by 65% to 82%, decreasing boll number per plant by 86% to 96% and single boll weight by approximately 24%. Weed-free seed cotton yields ranged from 2,900 to 3,130 kg ha−1, while yield loss increased with the duration of weed infestation, reaching up to 83% to 96% compared with permanent weed-free plots. Modified Gompertz and logistic models were used to analyze the impact of increasing weed control duration and weed interference on relative seed cotton yield (percentage of season-long weed-free cotton), respectively. Based on a 5% yield loss threshold, the CPWC was found to be from 145 to 994 growing degree days (GDD), corresponding to 14 to 85 d after emergence (DAE). These findings emphasize the importance of implementing effective weed control measures from 14 to 85 DAE in the Lower Yangtze Valley to prevent crop losses exceeding a 5% yield loss threshold.
Temperature is the most significant abiotic factor that affects the growth and behaviour of insects. However, the mechanism by which the olfactory system senses thermal stimulus and combines temperature and chemical signals to trigger certain behavioural outputs is unclear. This study aimed to clarify the mechanism by which environmental temperature affects olfactory perception in Apis cerana cerana (Hymenoptera: Apidae). We used quantitative reverse-transcriptase polymerase chain reaction and western blotting to analyse the expression of AcerOr1 and AcerOr2. We also used electroantennography (EAG) assays to detect bee antennal responses to odorants at different temperatures. The results revealed that the mRNA expression of AcerOr1 and AcerOr2 was significantly influenced by temperature. These genes exhibited both increases and decreases in expression over time, with the most significant differential observed at 25 °C. Protein expression was similarly affected at 2 hours after different temperature treatments. Electroantennography responses from the antennae revealed that six odorant volatiles – N-(4-ethylphenyl)-2-((4-ethyl-5-(3-pyridinyl)-4H-1,2,4-triazol-3-yl)thio)acetamide (VUAA1), linolenic acid, eugenol, hexyl acetate, 1-nonanol, and lauroleic acid – had the most dramatic effect at 25 °C. The results indicate that environmental factors affecting the expression of AcerOr1 and AcerOr2 modulate olfactory recognition behaviour in A. cerana cerana, suggesting that changes in environmental temperature can affect bees’ olfactory preferences.
Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems.
The Early-Middle Jurassic impression/compression macroflora and the palynoflora from the Qaidam Basin in the northeastern Qinghai-Xizang (Tibetan) Plateau have been well studied; however, fossil wood from this region has not been previously documented systematically. Here, we describe an anatomically well-preserved fossil wood specimen from the Lower Jurassic Huoshaoshan Formation at the Dameigou section in northern Qinghai Province, northwestern China. This fossil exhibits typical Metapodocarpoxylon Dupéron-Laudoueneix et Pons anatomy with usually araucarian radial tracheid pits and variable cross-field pits, representing a new record for Metapodocarpoxylon in the Qaidam Basin. This discovery indicates that trees with this type of wood anatomy were not confined to northern Gondwana but also grew in more northerly regions in Laurasia. The wood displays distinct growth rings, with abundant, well-formed earlywood and narrow latewood. This observation, along with previous interpretations based on macroflora, palynoflora and sedimentological data, suggests that a warm and humid climate with mild seasonality prevailed in the region during the Early Jurassic.
Kim Dong-ri and Seo Jeong-ju were prodigious personalities of Korean literature in the 20th century. The friendship of these prominent writers began in the 1930s and lasted their entire lives. Together they contributed to the reconstruction of the literary world in Korea after the end of the Japanese Occupation. This paper examines their biographies, auto-biographies, and different testimonies in an attempt to understand and appreciate not only the development and depth of their friendship, but also their legacy to Korean literature that was based on the literary and humanistic values they shared.
This study investigated how interlocutors’ linguistic competence affected L2 speakers’ lexical alignment and how the interlocutor effect was modulated by speakers’ proficiency. Chinese English as a Foreign Language speakers performed an online text-based picture-naming and -matching task with interlocutors of different perceived linguistic competences: an L1 interlocutor, an L2 interlocutor of higher proficiency or an L2 interlocutor of lower proficiency. We compared the magnitudes of L2 speakers’ lexical alignment across interlocutor conditions and examined whether the interlocutor effect varied with speakers’ L2 proficiency. Results showed that L2 speakers aligned more with the L1 interlocutor than the L2 interlocutor, indicating an effect of interlocutors’ language status (native vs. non-native). Moreover, L2 speakers’ lexical alignment with interlocutors of varying proficiency was differentially affected by their own proficiency levels. This study adds to the existing work by revealing the joint influence of the interlocutors’ competence and the speakers’ L2 proficiency on lexical alignment in L2 communication.
Parkinson disease (PD) is a neurological disorder that affects more than 10 million people worldwide, impacting both quality of life and marital relationships. Divorce rates are higher in Western compared to Eastern countries. However, it is unclear if there are differences in marital status after PD onset between the two regions.
Methods:
We searched MEDLINE and Embase from inception to March 31, 2024. Original studies discussing marital status in participants with PD were included. An unmarried status includes single, widowed, divorced or separated. Outcomes included the prevalence of being unmarried, measured as proportions, and the risk ratio (RR) of being unmarried in participants with PD relative to controls.
Results:
Out of 567 studies screened initially, 55 studies involving 3,723,966 participants were included. The prevalence of being unmarried was evaluated in 55 studies (25.16%; 95% CI: 21.52–29.18). The prevalence of being unmarried was significantly higher in countries in the West compared to the East (28.83%; 95% CI: 25.09–32.89 vs 17.47%; 95% CI: 12.10–24.57, p < 0.01). Compared to controls, the risk of being unmarried in PD participants after the onset of PD was significantly higher in the East (RR: 1.21, 95% CI: 0.91–1.60) compared to the West (RR: 0.87, 95% CI: 0.65–1.15).
Conclusions:
Our meta-analysis showed significantly higher rates of being unmarried in Western compared to Eastern countries. After PD onset, participants in the East were at significantly higher risk of being unmarried compared to participants in the West, suggesting that differences in cultural practices, societal norms and healthcare systems may affect marital status outcomes in PD participants.
Compared with first-tier cities in China that are of abundant funds and resources like legions of high-level hospitals, the degree of nurses’ disaster nursing preparedness in non-first-tier cities (inland) is relatively lower. For example, nurses’ knowledge reserve of specific disasters is not comprehensive enough. And nurses are diffident when it comes to the skills of handling disaster rescue. Furthermore, their understanding of the roles to take in disaster coordination management is ambiguous. Conquering these challenges could be conducive to the improvement of local medical staffs’ confidence and capabilities in disaster relief. Consequently, the objective of this research is to probe for approaches of improving the strategies of disaster nursing in Chinese small cities.
Methods
In order to ascertain the factors that influence disaster preparedness, a cross-sectional study with SPSS 25.0 data analysis method is adopted. The sample of the study is nursing personnel from 4 comprehensive hospitals at or above the second level in Yongcheng, Henan Province, China, as nurses are first-line responders in disasters and pandemics and the largest group in disaster rescue.
Results
From 813 distributed questionnaires, 784 completed questionnaires were returned, for a response rate of 96.43%. The total average score of the Disaster Preparedness Evaluation Tool is 146.76 ± 34.98, and the average score of all the entries is 3.26 ± 0.45, indicating moderate performance. The results indicate that age affects post disaster management (P < 0.05), meaning that as age increases, the scores also tend to be higher. Work experience, professional titles, disaster relief experience, and disaster training significantly differ in terms of knowledge, skills, post disaster management, and total scores (P < 0.01). The result of multivariate analysis indicates that titles, disaster relief experiences, and disaster training are the main factors affecting the disaster preparedness of nurses in Chinese non-first-tier (inland) cities(P < 0.05).
Conclusions
For the sake of upgrading the efficiency of disaster nursing preparedness in Chinese non-first-tier (inland) cities with limited funds and resources, it is very important to formulate training and education methods that are suitable for the local area, conduct characteristic simulation exercises, and expand experience exchange between hospitals. It’s certain that the local government will also play an important role in coordinating and organizing the division of labor, resource allocation, and management of hospitals at all levels in different phases of disasters, which can help nursing staff have a clearer understanding of their roles when preparing for disasters.
With increased global navigation satellite system (GNSS) signals and degraded observation environments, the correctness of ambiguity resolution is disturbed, causing unexpected real-time kinematic (RTK) positioning solutions. This paper presents an improved fault detection and exclusion (FDE) method based on the generalized least squares (GLS) model. The correlated GLS model is constructed by regarding double-differencing (DD) integer ambiguities as the known parameters. Meanwhile, the validity of residuals as crucial components of fault detection could be enhanced by the iterative re-weighted least squares (IRLS) method rather than the least squares (LS) without robustness. A static test with artificial faults and a dynamic test with natural faults were carried out, respectively. By analyzing test statistics of the enhanced FDE algorithm and comparing its positioning errors with those from the classical LS, it is shown that our method can provide high-precision and high-reliability RTK solutions facing wrong DD fixed ambiguities due to observation faults.
This study aimed to develop a predictive tool for identifying individuals with high antibody titers crucial for recruiting COVID-19 convalescent plasma (CCP) donors and to assess the quality and storage changes of CCP. A convenience sample of 110 plasma donors was recruited, of which 75 met the study criteria. Using univariate logistic regression and random forest, 6 significant factors were identified, leading to the development of a nomogram. Receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) evaluated the nomogram’s discrimination, calibration, and clinical utility. The nomogram indicated that females aged 18 to 26, blood type O, receiving 1 to 2 COVID-19 vaccine doses, experiencing 2 symptoms during infection, and donating plasma 41 to 150 days after symptom onset had higher likelihoods of high antibody titres. Nomogram’s AUC was 0.853 with good calibration. DCA showed clinical benefit within 9% ~ 90% thresholds. CCP quality was qualified, with stable antibody titres over 6 months (P > 0.05). These findings highlight developing predictive tools to identify suitable CCP donors and emphasize the stability of CCP quality over time, suggesting its potential for long-term storage.