Ancient medicine meets modern trials in diabetic kidney disease care

West China Hospital of Sichuan University

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image: 

SZF ameliorates renal injury in mice by regulating CX3CL1 and MCP-1 expression. (A) Representative images of H&E staining; (B) Masson’s trichrome staining; (C) PAS staining; (D) western blot analysis of CX3CL1 and MCP-1; (E) RT-qPCR analysis of CX3CL1 and MCP-1. n = 3. Compared with the Sham group: ##P < 0.01; ###P < 0.001. Compared with the model group: *P < 0.05; **P < 0.01; ***P < 0.001.

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Credit: Precision Clinical Medicine

Diabetic kidney disease is a leading cause of kidney failure worldwide, yet current treatments often slow progression sustained improvement in kidney function being uncommon. A new randomized clinical study evaluated whether a traditional multi-herb formulation could offer comparable or superior benefits to standard therapy. The research found that while both treatments similarly reduced urinary protein levels, the herbal formula significantly improved key indicators of kidney function, including estimated glomerular filtration rate and serum creatinine. Beyond clinical outcomes, the study also explored underlying biological mechanisms, revealing that the treatment modulates inflammatory pathways linked to kidney damage. These findings suggest that targeting inflammation may offer a complementary strategy for preserving renal function in diabetic kidney disease.

Diabetic kidney disease affects a growing proportion of people with diabetes and remains the leading cause of end-stage renal disease. Clinically, the condition is marked by persistent proteinuria and a gradual decline in kidney filtration capacity. Current first-line therapies, such as angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, typically slow renal function decline; however, sustained improvement is uncommon, and treatment-related adverse effects may limit long-term use. Increasing evidence suggests that chronic inflammation plays a central role in driving kidney fibrosis and functional loss in diabetes. Based on these challenges, there is a need to explore alternative or complementary treatments that can protect renal function by targeting inflammatory mechanisms.

Researchers from Guang'anmen Hospital of the China Academy of Chinese Medical Sciences, together with collaborators from multiple traditional Chinese medicine hospitals across China, reported (DOI: 10.1093/pcmedi/pbaf031) on November 14, 2025, in Precision Clinical Medicine the results of a multicenter, randomized, double-blind clinical trial evaluating a traditional Chinese herbal formula for diabetic kidney disease with macroalbuminuria. The 24-week study enrolled 120 patients and compared the therapy with the angiotensin receptor blocker irbesartan, assessing renal outcomes, safety, and symptom improvement. Mechanistic investigations combining proteomics, single-cell transcriptomics, and animal models were conducted to uncover inflammation-related pathways underlying the clinical effects.

The trial showed that both treatments achieved similar reductions in 24-hour urinary protein, a standard marker of kidney damage. However, patients receiving the herbal formulation experienced significantly better preservation of kidney function. Their estimated glomerular filtration rate increased over the treatment period, while it declined in the comparison group, and serum creatinine levels decreased rather than rising. Bayesian statistical analysis further supported a high probability that the herbal therapy provided meaningful renal benefits.

To understand why these differences occurred, the researchers conducted mechanistic studies. Olink inflammation proteomic profiling identified significant reductions in circulating inflammatory mediators, particularly CX3CL1 and MCP-1, following treatment. Single-nucleus RNA sequencing of diabetic mouse kidneys revealed that these molecules are predominantly expressed in endothelial, mesangial, and tubular cells—key players in kidney inflammation and fibrosis. Treatment suppressed their expression in specific cell populations, suggesting a cell-type–resolved anti-inflammatory effect.

Animal experiments reinforced these findings. Mice receiving the herbal therapy showed improved renal biochemical markers and reduced structural damage, including less fibrosis and mesangial expansion. Together, the clinical and experimental results indicate that the therapy may protect kidney function by dampening inflammation-driven injury rather than acting solely through hemodynamic control.

"The most striking aspect of this study is the improvement in kidney function, not just stabilization," said one of the corresponding investigators. "Many existing therapies slow decline, but few demonstrate an actual increase in filtration capacity. By integrating clinical trials with molecular and single-cell analyses, we were able to link these functional benefits to specific inflammatory pathways. This systems-level approach strengthens confidence that the observed effects are biologically meaningful and not simply statistical variation."

If confirmed in longer and larger trials, these findings could expand treatment options for patients with diabetic kidney disease, particularly those who cannot tolerate standard medications or remain at high residual risk. The study highlights inflammation as a viable therapeutic target and suggests that multi-component therapies may exert synergistic effects on complex disease pathways. Beyond this specific formulation, the work provides a framework for integrating traditional medicine with modern clinical trials and omics technologies. Such approaches could accelerate the discovery of complementary therapies aimed at preserving kidney function and improving quality of life for millions of patients worldwide.

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References

DOI

10.1093/pcmedi/pbaf031

Original Source URL

https://doi.org/10.1093/pcmedi/pbaf031

Funding information

This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (grant No. 2023ZD0509300), the National Natural Science Foundation of China (grant No. 82505276) and the Clinical Research Fund of the Central High-level Hospital of Traditional Chinese Medicine (grant No. HLCMHPP2023084).

About Precision Clinical Medicine

Precision Clinical Medicine (PCM) commits itself to the combination of precision medical research and clinical application. PCM is an international, peer-reviewed, open-access journal that publishes original research articles, reviews, clinical trials, methodologies, perspectives in the field of precision medicine in a timely manner. By doing so, the journal aims to provide new theories, methods, and evidence for disease diagnosis, treatment, prevention and prognosis, so as to establish a communication platform for clinicians and researchers that will impact practice of medicine. The journal covers all aspects of precision medicine, which uses novel means of diagnosis, treatment and prevention tailored to the needs of a patient or a sub-group of patients based on the specific genetic, phenotypic, or psychosocial characteristics. Clinical conditions include cancer, infectious disease, inherited diseases, complex diseases, rare diseases, etc. The journal is now indexed in ESCI, Scopus, PubMed Central, etc., with an impact factor of 5.0 (JCR2024, Q1).

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Journal

Precision Clinical Medicine

DOI

10.1093/pcmedi/pbaf031 

Subject of Research

Not applicable

Article Title

Randomized controlled clinical trial of Shenzhuo Formula in the treatment of macroalbuminuria in diabetic kidney disease and its inflammation-modulating mechanisms

 

Fires could emit more air pollution than previously estimated

American Chemical Society

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As fires burn the landscape, they spew airborne gases and particles, though their impact on air pollution might be underestimated. A study in ACS’ Environmental Science & Technology reports that, around the world, wildfires and prescribed burns (i.e., wildland fires) could emit substantially more gases, including ones that contribute to air pollution, than previously thought. The researchers identified several regions with high wildland fire and human activity emissions, which may pose complex air-quality challenges.

“Our new estimates increase the organic compound emissions from wildland fires by about 21%,” says Lyuyin Huang, the first author of the study. “The inventory provides a foundation for more detailed air-quality modeling, health-risk assessment and climate-related policy analysis.”

Each year, large swaths of forests, grass and peat burn in wildfires, releasing a complex mix of water vapor, ash and carbon-based compounds into the air. Some of these carbon-based compounds are gases called volatile organic compounds (VOCs). Others that evaporate and turn into gases at warmer temperatures are known as intermediate- and semi-volatile organic compounds (IVOCs and SVOCs, respectively). And in the air, these partially-volatile compounds form fine particles — pollutants that can be harmful if breathed in — more easily than VOCs. However, most studies assessing wildland fire emissions overlook IVOCs and SVOCs because of their large number, which makes it hard to measure these compounds. Researchers led by Shuxiao Wang wanted to take IVOCs and SVOCs emissions along with VOCs into consideration to offer better insight into wildland fires’ impact on air quality, health and climate.

First, the researchers accessed a database of the burned land area for global forest, grass and peatland wildland fires from 1997 to 2023. They also collected data on the VOCs, IVOCs, SVOCs, and other extremely low volatility organic compounds emitted as each vegetation type burns. For vegetation types without field measurements, they relied on laboratory experiments to predict the organic compounds released. Then, the team combined these datasets and calculated annual emissions around the world.

Altogether, the researchers estimated wildland fires released an average of 143 million tons of airborne organic compounds each year of the study. This amount is 21% higher than earlier estimates, suggesting that wildland fire emissions, specifically the IVOCs and SVOCs, could cause more air pollution than previously thought.

Comparing wildland fire emissions to their earlier estimate of human activities that release airborne compounds, the researchers found that the human-caused emissions were greater overall, but both sources released equivalent amounts of IVOCs and SVOCs. Additionally, multiple emission hotspots for both wildfire and human activity emerged from the comparison: Equatorial Asia, Northern Hemisphere Africa and Southeast Asia. The researchers say these regions’ air pollution challenges are complex, requiring different strategies to reduce emissions from fires and human activities.

The authors acknowledge funding from the National Natural Science Foundation of China, National Key R&D Program of China, the Samsung Advanced Institute of Technology, and the Center of High Performance Computing at Tsinghua University.

The paper’s abstract will be available on Dec. 29 at 8 a.m. Eastern time here: http://pubs.acs.org/doi/abs/10.1021/acs.est.5c10217

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The American Chemical Society (ACS) is a nonprofit organization founded in 1876 and chartered by the U.S. Congress. ACS is committed to improving all lives through the transforming power of chemistry. Its mission is to advance scientific knowledge, empower a global community and champion scientific integrity, and its vision is a world built on science. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, e-books and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

Registered journalists can subscribe to the ACS journalist news portal on EurekAlert! to access embargoed and public science press releases. For media inquiries, contact newsroom@acs.org.

Note: ACS does not conduct research but publishes and publicizes peer-reviewed scientific studies.

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Journal

Environmental Science & Technology

DOI

10.1021/acs.est.5c10217 

Article Title

Global Wildland Fire Emissions of Full-Volatility Organic Compounds from 1997 to 2023

Article Publication Date

29-Dec-2025

NEGATION OF THE NEGATION

How doubting your doubts may increase commitment to goals

Research explores what happens when people face goal obstacles

Ohio State University

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COLUMBUS, Ohio – When it comes to our most important long-term goals in life, it is not uncommon to face obstacles that may lead us to doubt whether we can achieve our ambitions.

 

But when life hands you doubts, the answer may be to question your doubts, a new study suggests.

 

A psychology professor found that when people who were worried about achieving an identity goal were induced to experience what is called meta-cognitive doubt, they actually became more committed to achieving their goal.

 

“What this study found is that inducing doubts in one’s doubts can provide a formula for confidence,” said Patrick Carroll, author of the study and professor of psychology at The Ohio State University at Lima.

 

The study was published online recently in the journal Self and Identity.

 

Carroll was interested in what happens when people have what is called an “action crisis” while pursuing an identity goal – a long-term objective centered on who you want to become in life. Wanting to become a doctor, for instance, is an identity goal.

 

An action crisis is a decision conflict where you are not sure if you want to continue pursuit of the goal.

 

“When you’re pursing identity goals, bumps in the roads inevitably arise. There may come a point where the obstacle is big enough to evoke doubts about whether to continue,” Carroll said.

 

Most research on the topic has focused specifically on these doubts and how they can impact whether people go forward with their goals.

 

But based on previous work done by other Ohio State researchers, Carroll decided to examine meta-cognitive doubt, which is the sense of certainty a person has in the validity of one’s thoughts.

 

In the case of this research, a person can have doubts about whether they can achieve their goal. But what happens if you make the person wonder if their doubts are valid?

 

Carroll conducted two studies.  One involved 267 people who participated online.  First, they completed an action crisis scale about their most important personal goal. The scale included items such as “I doubt whether I should continue striving for my goal or disengage from it” and participants responded on a scale from “strongly disagree” to strongly agree.”

 

Participants were then told they would take part in a second, unrelated study on the effect of memory writing exercises. Half of the participants were asked to write about a time that they felt confidence in their thinking.  The other half were asked to write about a time when they had experienced doubt in their thinking.

 

After completing the writing exercise, all participants were asked to rate how committed they were to achieving their most important personal goal, on a scale from “not at all committed” to “very committed.”

 

Findings showed that the writing exercise succeeded in making people feel more confident or more doubtful in their own thoughts about their identity goal – even though the writing exercise was not directly connected to their goals.

 

Here’s how it worked: Those participants who felt doubtful about their identity goal – and then wrote about an experience feeling confident – were less committed to achieving their goal. In other words, the writing exercise made them more confident in their doubts about achieving their goal.

 

On the other hand, those who felt doubtful about their goal and then wrote about an experience of feeling doubtful in their own thoughts actually had higher levels of commitment to their goals. For them, writing about doubt made them question their own doubts about achieving their goal.

 

“On some level, it may seem that doubt would be additive. Doubt plus doubt would equal more doubt,” Carroll said. “But this study found the opposite: Doubt plus doubt equaled less doubt.”

 

Carroll replicated the findings in another study, involving 130 college students, that used a different way of inducing doubt.  In this study, Carroll used a technique developed by Ohio State researchers that had the participants complete the action crisis scale with their non-dominant hand.

 

“Previous research showed that using the non-dominant hand leads participants to have doubts in their own thoughts because they use their shaky handwriting as a cue that their thoughts must be invalid,” Carroll said.

 

“And that is exactly what I found in this study. So in two different studies we found that inducing meta-cognitive doubt can lead to people doubting their own doubts.”

 

On a practical level, it may be difficult for individuals to induce doubts about their doubts on their own, Carroll said.  One reason it worked in this study is that participants were not aware that the doubt induction was related to their goal doubts.

 

This could be more effective if someone else – a therapist, a teacher, a friend or a parent – can help a person question their own thoughts and doubts.

 

“You don’t want the person to be aware that you’re getting them to question their doubts about their goals,” he said.

 

Carroll also noted that this technique should be used carefully, because it could potentially undermine wise judgment if overused or misapplied.

 

“You don’t want to undermine humility and replace it with overconfidence or premature certainty,” he said.  “This needs to be used wisely.”

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Journal

Self and Identity

DOI

10.1080/15298868.2025.2597804 

Method of Research

Experimental study

Subject of Research

People

Article Title

Increasing identity goal commitment by inducing doubt in goal doubts

 

Beyond vegetation indices: A phenomic prediction strategy sharpens genetic signals from drone-based crop imaging

Nanjing Agricultural University The Academy of Science

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image: 

Blue boxes represent the final genome-wide association study and are identical between the four approaches. The differences lie in the input of the response variable: A) the input is the visual score (VS) assigned by trained staff and adjusted for experimental design; B) replaces the VS by an individual specific vegetation index (VI) adjusted for experimental design; C) uses a phenomic prediction approach to predict the VS from phenomic data with a trained prediction model; D) instead of phenotypes adjusted to the experimental design only, genomic estimated breeding values (GEBVs) are used in the phenomic prediction of VS.

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Credit: The authors

By training statistical and machine-learning models to predict expert visual scores, the study demonstrates that phenomics can match or outperform traditional indices, particularly when combined with genomic information to strengthen genetic analyses.

Remote sensing has transformed crop phenotyping, but disease assessment from drone imagery still often relies on a single vegetation index, which can miss important biological signals. UAV-based high-throughput phenotyping now plays a central role in crop breeding, as multispectral and thermal sensors enable rapid, non-destructive monitoring of plant health. These complex datasets are typically reduced to indices such as NDVI or simple reflectance ratios, each capturing only part of plant physiology. However, index performance varies across environments and disease conditions, especially when symptoms are confounded by stress or canopy structure. Phenomic prediction provides an alternative by integrating multiple traits to predict target phenotypes, raising the question of whether such predictions can improve genetic analyses such as genome-wide association studies.

A study (DOI: 10.1016/j.plaphe.2025.100134) published in Plant Phenomics on 5 November 2025 by Johannes W.R. Martini’s team, International Maize and Wheat Improvement Center-CIMMYT, shows phenomic prediction combining remote sensing and genomic breeding values outperforms single vegetation indices in disease phenotyping and genetic analysis.

The study systematically evaluated whether phenomic prediction can outperform individual vegetation indices in disease resistance phenotyping by integrating UAV-derived multispectral and thermal data with statistical and machine-learning models. As a benchmark, absolute correlations between 15 remote-sensing traits and human-assigned visual scores (VS) were calculated across six population-by-year datasets, identifying the green-to-red reflectance ratio (G) as the strongest correlate in five cases, and NDVI in one. Building on this reference, researchers trained phenomic models to predict VS using two predictor sets—five basic spectral wavelengths and an expanded set including ten vegetation indices—while comparing linear ordinary least squares (OLS), regularized regressions (ridge regression and LASSO), and non-linear approaches (artificial neural networks, ANN; gradient boosted regression trees, GBRT). Using design-adjusted phenotypes, a simple OLS model based on five wavelengths (BT-OLS) matched or exceeded the benchmark correlation in 13 of 30 out-of-set predictions and outperformed vegetation index G in GWAS signal strength in 14 cases, typically identifying the same major resistance locus. In contrast, extending OLS to all 15 traits led to pronounced overfitting, with good performance confined to training data and weak generalization. Regularization mitigated this problem: ridge regression and LASSO applied to all traits (AT-RR, AT-LASSO) exceeded the benchmark in 18–19 of 30 cases and outperformed G in up to 16 GWAS comparisons, while non-linear ANN models provided only marginal additional gains and GBRT performed comparatively poorly on phenotypic data. The most substantial improvement emerged when phenotypes were replaced with genomic estimated breeding values (GEBVs), which isolate additive genetic effects and reduce environmental noise. When phenomic models were trained on GEBVs, GWAS signals became markedly stronger and more consistent: AT-RR based on GEBVs outperformed vegetation index G in 26 of 30 cases while pinpointing nearly identical loci with higher statistical power, and AT-ANN-GEBV showed similarly strong, though slightly weaker, gains. Binomial tests confirmed that GEBV-based phenomic models clearly outperformed phenotype-based approaches, highlighting genomic adjustment as the primary driver of enhanced genetic signal detection.

The findings suggest a shift in how remote sensing data should be used in breeding and genetic research. Rather than searching for the “best” vegetation index, breeders and geneticists can deploy phenomic models as flexible, data-driven indices tailored to specific traits and crops. This approach is especially valuable when disease symptoms are subtle or variable across environments.

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References

DOI

10.1016/j.plaphe.2025.100134

Original Source URl

https://doi.org/10.1016/j.plaphe.2025.100134

About Plant Phenomics

Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.

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Journal

Plant Phenomics

DOI

10.1016/j.plaphe.2025.100134 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Comparing statistical ‘phenomic prediction’ models for remote-sensing-based phenotyping of maize susceptibility to common rust